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Collaborative Filtering Using Data Mining and Analysis Vishal Bhatnagar
Collaborative Filtering Using Data Mining and Analysis Vishal Bhatnagar Ambedkar Institute of Advanced Communication Technologies and Research, India A volume in the Advances in Data Mining and Database Management (ADMDM) Book Series
Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2017 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: eresources@igi-global.com. Names: Bhatnagar, Vishal, 1977- author. Title: Collaborative filtering using data mining and analysis / Vishal Bhatnagar, editor. Description: Hershey, PA : Information Science Reference, [2017] | Includes bibliographical references and index. Identifiers: LCCN 2016012755| ISBN 9781522504894 (hardcover) | ISBN 9781522504900 (ebook) Subjects: LCSH: Data mining. | Recommender systems (Information filtering) | Multiagent systems. Classification: LCC QA76.9.D343 C747 2017 | DDC 006.3/12--dc23 LC record available at https://lccn.loc.gov/2016012755 This book is published in the IGI Global book series Advances in Data Mining and Database Management (ADMDM) (ISSN: 2327-1981; eISSN: 2327-199X)
The Advances in Data Mining and Database Management (ADMDM) Book Series (ISSN 2327-1981) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http:// www.igi-global.com/book-series/advances-data-mining-database-management/37146. Postmaster: Send all address changes to above address. Copyright © 2017 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or informa- tion and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global. IGI Global is currently accepting manuscripts for publication within this series. To submit a pro- posal for a volume in this series, please contact our AcquisitionEditorsatAcquisitions@igi-global.com or visit: http://www.igi-global.com/publish/. • Data warehousing • Sequence Analysis • Quantitative Structure–Activity Relationship • Profiling Practices • Data Analysis • Neural Networks • Database Testing • Cluster Analysis • Web-based information systems • Information Extraction Coverage With the large amounts of information available to organizations in today’s digital world, there is a need forcontinualresearchsurroundingemergingmethodsandtoolsforcollecting,analyzing,andstoringdata. The Advances in Data Mining Database Management (ADMDM) series aims to bring together research in information retrieval, data analysis, data warehousing, and related areas in order to become an ideal resource for those working and studying in these fields. IT professionals, software engineers, academicians and upper-level students will find titles within the ADMDM book series particularly use- ful for staying up-to-date on emerging research, theories, and applications in the fields of data mining and database management. Mission ISSN: 2327-1981 EISSN: 2327-199X David Taniar Monash University, Australia Advances in Data Mining and Database Management (ADMDM) Book Series
Titles in this Series For a list of additional titles in this series, please visit: www.igi-global.com Intelligent Techniques for Data Analysis in Diverse Settings Numan Celebi (Sakarya University, Turkey) Information Science Reference • copyright 2016 • 353pp • H/C (ISBN: 9781522500759) • US $195.00 (our price) Managing and Processing Big Data in Cloud Computing Rajkumar Kannan (King Faisal University, Saudi Arabia) Raihan Ur Rasool (King Faisal University, Saudi Arabia) Hai Jin (Huazhong University of Science and Technology, China) and S.R. Balasundaram (National Institute of Technology, Tiruchirappalli, India) Information Science Reference • copyright 2016 • 307pp • H/C (ISBN: 9781466697676) • US $200.00 (our price) Handbook of Research on Innovative Database Query Processing Techniques Li Yan (Nanjing University of Aeronautics and Astronautics, China) Information Science Reference • copyright 2016 • 625pp • H/C (ISBN: 9781466687677) • US $335.00 (our price) Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence Noor Zaman (King Faisal University, Saudi Arabia) Mohamed Elhassan Seliaman (King Faisal University, Saudi Arabia) Mohd Fadzil Hassan (Universiti Teknologi PETRONAS, Malaysia) and Fausto Pedro Garcia Marquez (Campus Universitario s/n ETSII of Ciudad Real, Spain) Information Science Reference • copyright 2015 • 500pp • H/C (ISBN: 9781466685055) • US $285.00 (our price) Improving Knowledge Discovery through the Integration of Data Mining Techniques Muhammad Usman (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Pakistan) Information Science Reference • copyright 2015 • 391pp • H/C (ISBN: 9781466685130) • US $225.00 (our price) Modern Computational Models of Semantic Discovery in Natural Language Jan Žižka (Mendel University in Brno, Czech Republic) and František Dařena (Mendel University in Brno, Czech Republic) Information Science Reference • copyright 2015 • 335pp • H/C (ISBN: 9781466686908) • US $215.00 (our price) Mobile Technologies for Activity-Travel Data Collection and Analysis Soora Rasouli (Eindhoven University of Technology, The Netherlands) and Harry Timmermans (Eindhoven Uni- versity of Technology, The Netherlands) Information Science Reference • copyright 2014 • 325pp • H/C (ISBN: 9781466661707) • US $225.00 (our price) 701 E. Chocolate Ave., Hershey, PA 17033 Order online at www.igi-global.com or call 717-533-8845 x100 To place a standing order for titles released in this series, contact: cust@igi-global.com Mon-Fri 8:00 am - 5:00 pm (est) or fax 24 hours a day 717-533-8661
   Editorial Advisory Board Manish Kumar, IIIT Allahabad, India Rakesh E. M., IBM India, India Rashmi Malhotra, Saint Joseph’s University, USA Latesh Malik, G.H. Raisoni College of Engineering, India Tarun Srivastava, Deloitte India, India Golla Varaprasad, BMS College of Engineering, India Toyohide Watanabe, Nagoya Industrial Science Research Institute, Japan Jie Yan, Stanford University, USA Yudong Zhang, Columbia University, USA New York State Psychiatric Institute, USA
 Table of Contents  Foreword............................................................................................................................................... xv Preface. .................................................................................................................................................xvi Acknowledgment...............................................................................................................................xxiv Section 1 Data Mining Techniques and Analysis: An Overview Chapter 1 Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System.................................................................................................................................. 1 Renuka Mahajan, Amity University UP, India Chapter 2 Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value......... 24 Mamta Mittal, G. B. Pant Govt. Engineering College, India R. K. Sharma, Thapar University, India V.P. Singh, Thapar University, India Lalit Mohan Goyal, Bharati Vidyapeeth College of Enineering, India Chapter 3 Dimensionality Reduction Techniques for Text Mining........................................................................ 49 Neethu Akkarapatty, SCMS School of Engineering and Technology, India Anjaly Muralidharan, SCMS School of Engineering and Technology, India Nisha S. Raj, SCMS School of Engineering and Technology, India Vinod P., SCMS School of Engineering and Technology, India Section 2 Collaborative Filtering: An Introduction Chapter 4 History and Overview of the Recommender Systems........................................................................... 74 Venkatesan M., Government Arts College (Autonomous), India Thangadurai K., Government Arts College (Autonomous), India
 Chapter 5 A Classification Framework Towards Application of Data Mining in Collaborative Filtering........... 100 Neeti Sangwan, Maharaja Surajmal Institute of Technology, India Naveen Dahiya, Maharaja Surajmal Institute of Technology, India Chapter 6 Collaborative Filtering Based Data Mining for Large Data. ................................................................ 115 Amrit Pal, Indian Institute of Information Technology Allahabad, India Manish Kumar, Indian Institute of Information Technology Allahabad, India Chapter 7 Big Data Mining Using Collaborative Filtering.................................................................................. 128 Anu Saini, G.B. Pant Engineering College, India Section 3 Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering Chapter 8 Collaborative and Clustering Based Strategy in Big Data................................................................... 140 Arushi Jain, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Pulkit Sharma, Ambedkar Institute of Advanced Communication Technologies and Research, India Chapter 9 Association Rule Mining in Collaborative Filtering............................................................................ 159 Carson K. Leung, University of Manitoba, Canada Fan Jiang, University of Manitoba, Canada Edson M. Dela Cruz, University of Manitoba, Canada Vijay Sekar Elango, University of Manitoba, Canada Chapter 10 A Classification Framework on Opinion Mining for Effective Recommendation Systems................ 180 Mahima Goyal, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Chapter 11 Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach. ............................................................................................................................. 195 Sheng-Jhe Ke, National Sun Yat-Sen University, Taiwan Wei-Po Lee, National Sun Yat-Sen University, Taiwan
 Chapter 12 Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey....................................... 217 Marenglen Biba, University of New York Tirana, Albania Narasimha Rao Vajjhala, University of New York Tirana, Albania Lediona Nishani, University of New York Tirana, Albania Chapter 13 Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers............... 236 Snehlata Sewakdas Dongre, Ghrce Nagpur, India Latesh G. Malik, Ghrce Nagpur, India Chapter 14 Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review....................... 250 Lediona Nishani, University of New York Tirana, Albania Marenglen Biba, University of New York in Tirana, Albania Compilation of References................................................................................................................ 270 About the Contributors..................................................................................................................... 302 Index.................................................................................................................................................... 307
  Detailed Table of Contents  Foreword............................................................................................................................................... xv Preface. .................................................................................................................................................xvi Acknowledgment...............................................................................................................................xxiv Section 1 Data Mining Techniques and Analysis: An Overview Chapter 1 Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System.................................................................................................................................. 1 Renuka Mahajan, Amity University UP, India This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what’ can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what’ can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning. Chapter 2 Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value......... 24 Mamta Mittal, G. B. Pant Govt. Engineering College, India R. K. Sharma, Thapar University, India V.P. Singh, Thapar University, India Lalit Mohan Goyal, Bharati Vidyapeeth College of Enineering, India Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but
 they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets. Chapter 3 Dimensionality Reduction Techniques for Text Mining........................................................................ 49 Neethu Akkarapatty, SCMS School of Engineering and Technology, India Anjaly Muralidharan, SCMS School of Engineering and Technology, India Nisha S. Raj, SCMS School of Engineering and Technology, India Vinod P., SCMS School of Engineering and Technology, India Sentimentanalysisisanemergingfield,concernedwiththeanalysisandunderstandingofhumanemotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains. Section 2 Collaborative Filtering: An Introduction Chapter 4 History and Overview of the Recommender Systems........................................................................... 74 Venkatesan M., Government Arts College (Autonomous), India Thangadurai K., Government Arts College (Autonomous), India This Chapter analyzes the recommender systems, their history and its framework in brief. The current generationoffilteringtechniquesinrecommendationmethodscanbebroadlyclassifiedintothefollowing five categories. Techniques used in these categories are discussed in detail. Data mining algorithms techniques are implemented in recommender systems to filters user data ratings. Area of application of Recommender Systems gives broad idea and such as how it gives impact and why it is used in the e-commerce, Online Social Networks (OSN), and so on. It has shifted the core of Internet applications fromdevicestousers.Inthischapter,issuesandrecentresearchinrecommendersystemarealsodiscussed. Chapter 5 A Classification Framework Towards Application of Data Mining in Collaborative Filtering........... 100 Neeti Sangwan, Maharaja Surajmal Institute of Technology, India Naveen Dahiya, Maharaja Surajmal Institute of Technology, India Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommendations to the users, efficient and cost effective methods needs
 to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests. Chapter 6 Collaborative Filtering Based Data Mining for Large Data. ................................................................ 115 Amrit Pal, Indian Institute of Information Technology Allahabad, India Manish Kumar, Indian Institute of Information Technology Allahabad, India Size of data is increasing, it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collaborative filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, mapreducebasedcollaborativefilteringaresomeofthesetechniques.Chapteraddressesthesetechniques and some basics of collaborative filtering. Chapter 7 Big Data Mining Using Collaborative Filtering.................................................................................. 128 Anu Saini, G.B. Pant Engineering College, India Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering. Section 3 Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering Chapter 8 Collaborative and Clustering Based Strategy in Big Data................................................................... 140 Arushi Jain, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Pulkit Sharma, Ambedkar Institute of Advanced Communication Technologies and Research, India There is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to
 a certain set of parameters than to those in other groups. These groups are known as clusters. Cluster analysisisoneofthe maintasksin the field ofdatamining and isacommonly usedtechniqueforstatistical analysis of data. While big data collaborative filtering defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets such as viewpoints, multiple agents and pre-existing data about the users’ behavior stored in matrices. Collaborative filtering is especially required when a huge data set is present. Chapter 9 Association Rule Mining in Collaborative Filtering............................................................................ 159 Carson K. Leung, University of Manitoba, Canada Fan Jiang, University of Manitoba, Canada Edson M. Dela Cruz, University of Manitoba, Canada Vijay Sekar Elango, University of Manitoba, Canada Collaborativefilteringusesdataminingandanalysistodevelopasystemthathelpsusersmakeappropriate decisionsinreal-lifeapplicationsbyremovingredundantinformationandprovidingvaluabletoinformation users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF- Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering. Chapter 10 A Classification Framework on Opinion Mining for Effective Recommendation Systems................ 180 Mahima Goyal, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature reviewaboutthedifferenttechniquesandapplicationsofthisfield.Theprimarytechniquescanbeclassified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering
 Chapter 11 Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach. ............................................................................................................................. 195 Sheng-Jhe Ke, National Sun Yat-Sen University, Taiwan Wei-Po Lee, National Sun Yat-Sen University, Taiwan Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbors for a particular user and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbors. In this chapter, we present a data analytics approach that combines user preference and social trust for making better collaborative recommendation. The proposed approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can be used to enhance the recommendation performance in an adaptive way for different datasets without an iterative parameter-tuning process. Chapter 12 Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey....................................... 217 Marenglen Biba, University of New York Tirana, Albania Narasimha Rao Vajjhala, University of New York Tirana, Albania Lediona Nishani, University of New York Tirana, Albania Thisbookchapterprovidesastate-of-the-artsurveyofvisualdataminingtechniquesusedforcollaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysisofthestate-of-the-artvisual dataminingtechniquesusedby researchersaswellasintheindustry. Collaborativefilteringapproachesarepresentedalongwithananalysisofthestate-of-the-artcollaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering. Chapter 13 Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers............... 236 Snehlata Sewakdas Dongre, Ghrce Nagpur, India Latesh G. Malik, Ghrce Nagpur, India A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly.
 Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play important role here how the different classifiers work collaborative within the ensemble to achieve a goal. Chapter 14 Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review....................... 250 Lediona Nishani, University of New York Tirana, Albania Marenglen Biba, University of New York in Tirana, Albania People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research. Compilation of References................................................................................................................ 270 About the Contributors..................................................................................................................... 302 Index.................................................................................................................................................... 307
  Foreword  The book entitled Collaborative Filtering Using Data Mining and Analysis comes as a timely and badly needed volume covering the most recent developments in this rapidly growing area. Even a quick query on Google Scholar when searching for “recommender system” shows a highly convincing pattern: the number of hits in 2000 was 489, in 2005 went up to 2,310 and in 2015 reached 9,900. The area is growing rapidly and calls for new methodologies, innovative ways of thinking and efficient solutions, especially when coping with new areas of applications. Recommender systems grow not only in their number but alsointhelevelofsophisticationandengagementofadvancedinformationprocessingtechnologiesinclud- ing active involvement of Computational Intelligence, sentiment analysis, and text analysis, in general. The book is organized in the three main parts being focused on data mining, collaborative filtering, and applications in collaborative filtering. The first part covers some review material on data mining, clustering, and dimensionality reduction. Definitely, this type of material is highly pertinent to the volume, given the evolution of the area of recommender systems where we have been witnessing a well-motivated quest for advanced techniques of data analysis, especially those falling under the rubric of data mining and becoming indispensable when coping with heterogeneous sources of data includ- ing textual ones. The second part of the book is aimed at the presentation of material on collaborative filtering. Collaborating filtering is inherently linked with recommender systems and constitutes an area of numerous and intensive research endeavors. The four chapters forming this part are a good reflection of the current tendencies. In addition to some historical exposure of the topic, discussed is a certain classification framework showing a role of data mining in collaborative filtering. The challenge posed by the emergence of big data is elaborated in the two last chapters of this part. The third part of the volume looks at various application facets, including studies on association rules, clustering, big data, data streams, all present in the setting of collaborative filtering. The volume delivers a coherent, logically arranged, and up-to-date material addressing various facets of collaborative filtering and recommender systems. The Editor and the authors deserve thanks for producing a timely, well-structured, and insightful volume, which will appeal to a broad readership. Not only those interested in recommender systems could find this book useful but the material could be of tangible value to researchers in data mining, sentiment analysis, and decision-making. Witold Pedrycz University of Alberta, Canada xv
 Preface  Recommendersystemshavedevelopedinparallelwiththeweb.Withthedevelopmentofweb,theinforma- tion available online increased at an exponential rate. This information overload required a system which could remove redundant information and provide the most valuable information to a user in minimum time. Collaborative Filtering is one the most accurate and widely adopted approaches for providing such information. It has found its application in domains ranging from e-commerce and e-learning to social networks and web search. Owing to its vast field, techniques, and challenges pertaining to collaborative filtering requires it to be conglomerated at one place to understand its underlying principle, working and application in its entirety. Collaborative filtering finds its roots in data-mining. Data mining is finding hidden and unknown information from inside large databases. Data mining tools and techniques are finding its immense applications in the modern day. Collaborative filtering using data mining will widen the application area and more interest will be created in budding research- ers to pursue their research in the same. The implications of data mining can be understood by the fact that whether it’s a public or private sector organization, all are taking the advantage of the data mining tools and techniques to reveal the hidden and unknown information from the available data. This has been widened primarily because of the large or can we say terabyte of data which is collected by all the organizations over the year and they are confused as how to use such a bulk of data. The new and emerging areas of data mining techniques have surprised many researchers and business persons who are gaining a lot of hidden and unknown information for increasing their ROI. Collaborative Filtering is one the most accurate and widely-adopted approaches for providing such information. It has found its application in domains ranging from e-commerce and e-learning to social networks and web search. The primarily techniques of data mining are: 1. Classification: A supervised learning-based technique in which different items are classified into target classes. This technique is used in the cases where the exact prediction is required. In this, a training set is prepared that finds the association between the values of predictors and the target. The target is the value assigned to the class and the predictor is the value associated with the do- main whose target class needs to be found. The major classification techniques employed are Naïve Bayes Algorithm, Decision Tree and Support Vector Machines (SVM). This technique finds the significant application in the detection of credit card fraud, and suspicious emails. 2. Clustering: Cluster analysis, or clustering, is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to a set of parameters than to those in other groups. These groups are known as clus- ters. Cluster analysis is one of the main tasks in the field of data mining and is a commonly used xvi
Preface technique for the statistical analysis of data. Cluster analysis does not refer to an algorithm but an exercise that has to be undertaken on the given data set. Various algorithms can be used for cluster analysis. The algorithms are divided into various categories and they differ significantly in their idea of what a cluster is constituted of and how the clusters are identified. The most popular ideas on the basis of which clusters are defined and identified include groups with small distances among the constituent members, areas of the data space which are highly dense, intervals or particular distributions. Clustering is a multi-objective problem that it is a mathematical optimization prob- lem. A clustering algorithm consists of parameter settings such as a distance function, a density threshold (the number of clusters expected to be formed). Based on the available data set and the use of result as intended by the user, apt clustering algorithm may be used. 3. Association Rule Mining: In association rule mining, the association between item sets are con- sidered or found with the help of Support and Confidence. The Rule are framed according to the data values and corresponding relationship between them. 4. Neural Network: A Neural Network (NN) is used to recognize patterns in data. The data can be specified according to the different domains like Financial Fraud including Credit Card Fraud de- tection and phishing, etc. NNs are used for those problems where the exact solution is not required, such that this technique is not sensitive to errors. Some common types of NNs are Artificial Neural Networks (ANNs) and Multilayer Artificial Neural Networks (MNNs). 5. Genetic Algorithms: Genetic Algorithms (GAs) predict using generated logic rules and fitness functions in order to detect financial fraud and suspicious e-mails. The major steps used are Mutation, Inheritance, Selection and Crossover. GAs and NNs can be used in combination to solve a complex problem. Every model inherits traits from previous models and compares it with the other models to more accurately model remains. It is based on the theory of the survival of the fittest, which means that the model which is fit will survive to the next generation and the others will not be applied to the next level. These techniques are able to classify the given data on the basis of whether it is supervised or unsu- pervised learning methodologies. In case of supervised learning, the dependent and independent vari- ables are considered. There are a set of independent variables based on which the value of the dependent variable is predicted, while in the case of unsupervised learning, the useful information is searched by forming clusters or groups. The variables in both cases can be nominal, ordinal, categorical or continu- ous variables depending upon the available data which enables us to apply the various algorithms of the different techniques discussed above. Collaborative filtering finds its roots in data-mining. Data mining is finding hidden and unknown information from large databases. The data mining tools and techniques are finding its immense applications in the modern day. Such an application is being proposed by the editor of this book which aims to find the data mining applications in emerging areas. These areas are already hot topics in the research. By including data mining in such areas, the application and usability of all said areas will be widened. The researchers are already working in the area of Collaborative filter- ing using the traditional methodologies. The editors are finding the data mining applications in this field with a motive of developing an effective recommendation system with accurate and precise information at the disposal of the users. Collaborative filtering is defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets, such as viewpoints, multiple agents and pre-existing data xvii
Preface about the users’ behavior stored in matrices. Collaborative filtering is required when a huge data set is present. The collaborative filtering methods are used to create recommender systems for a wide variety of fields with lots of data having varied formats, such as sensing and monitoring of data in battlefields, line of controls and mineral exploration; financial data of institutions that provide financial services, such as banks and stock markets; sensing of large geographical areas from which data is received from all kinds of sensors and activities; ecommerce and websites where the focus is to recommend products to users to increase sales, to name a few. A definition of collaborative filtering, which is somewhat newer and a bit narrow in sense states that it is a way of automating the process of making predictions, a process which is known as filtering, about the preferences and dislikes of a user by collecting data from as big a number of users as possible, a process which is known as collaborating, hence the name collaborative filtering. The underlying as- sumption of the collaborative filtering approach is that if a person A has the same opinion of an issue as a person B, then A is more likely to have an opinion similar to B’s opinion on a related but different issue. It is noteworthy that such predictions are specific to the user, but they are formed by using data from a number of users. The personal information of the user such as age, gender and location are generally not used in collaborative filtering (CF) but a partially observed matrix of ratings is used. The rating matrix may be binary or ordinal. The binary matrix contains the ratings by the users in columns in the form of likes or dislikes while the user’ name or id is in the rows. The ordinal matrix contains ratings in form of a number of responses from the user such as excellent, very good, good, average, poor or simply in form of stars out of five or ten, a system that is used frequently in this day and age. The rating matrix can easily be gathered implicitly by the website’s server, for example using click stream logging. Clicks on links to pages of goods or services provided can be considered to be a positive review of the user. While the rating matrices can prove to be useful, one major drawback is that they are extremely sparse, so it is very difficult to clump similar users together into classes. This is due to each and every user does not give reviews about each and every product. Thus, collaborative filtering consists of storing this sparse data and analyzing it to create a recommendation system. The objective of the proposed publication was to make aware researchers and other prospective read- ers with latest trends and patterns in the inclusion of the data mining tools and techniques in the areas of Collaborative filtering which helps to develop a system with precise knowledge and accuracy for helping the users of the system. The inclusion of improved and proven algorithms of the data mining helps to extract the nuggets of hidden and unknown information which helps to frame an effective recommenda- tion system using Collaborative filtering. The mission of the proposed publication was to come up with an edited book which aims at being the latest and most advanced topic inclusion and simultaneously acts as a discussion of the contributions of renowned researchers whose work has created a revolution in this area. The contributions by eminent researchers in fields of data mining, opinion mining, senti- ment analysis and Collaborative filtering will be part of book in emerging e-areas like retail, financial institutions and social networks. The objective would be to cover each and every aspect of Collaborative filtering, such as memory-based, model-based and Hybrid methodologies. The unique characteristics of the publication were: 1. The proposed work of eminent researchers in the aspect of Collaborative filtering—like memory- based, model-based and Hybrid methodologies—in areas such as retail, financial institutions and social networks which are current focuses of research will be part of the proposed publication. xviii
Preface 2. The proposed publication will be targeted towards providing the highest quality, most accurate and latest research by eminent researchers considering the facts of how such research affects and influ- ences common people in their everyday lives with effective and precise recommendation systems. 3. The area which will be part of published work will have a significant influence on business users, common people and have a great impact on society. In August 2015, in the call for chapters, I urged and sought contributions to this book from researchers, IT savvy’s, and young Engineers across the globe with an aim to extract and accumulate the modern day research in the field of Collaborative Filtering Using Data Mining and Analysis, and gradually I started receiving quality and very conceptual, basic and advanced contributions from different contributors from across the globe. Initially, I thought as whether I will be getting any chapters on this topic as it is very new and emerging area, but surprisingly I saw a great response with authors started to respond, which encouraged me and motivated me by showing that this area is gaining importance. After screening through them, my objective was clear, this aimed and concentrated on getting chapters which focused on elementary issues, needs, and the demand for Collaborative Filtering. The book is a collection of the fourteen chapters which have been written by eminent professors, researchers, and industry people from different countries. These chapters were initially peer-reviewed by the Editorial board members, reviewers, and industry people who themselves span over many countries. The book is divided into three sections: Section 1, Data Mining techniques and analysis: An Overview; Section2,Collaborativefiltering:AnIntroduction;andSection3,Applicationsofdataminingtechniques and data analysis in collaborative filtering. SECTION 1: DATA MINING TECHNIQUES AND ANALYSIS: AN OVERVIEW Chapter 1 by Dr. Renuka Mahajan, revolves around the synthesis of three research areas- data min- ing, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what’ can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what’ can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning. Chapter 2 by Mamta Mittal, Dr. R.K. Sharma, Dr. V.P. Singh and Lalit Mohan Goyal enlightened that Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired xix
Preface distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five ex- periments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets. In Chapter 3 by Neethu Akkarapatty, Anjaly Muralidharan, Nisha S. Raj and Dr. Vinod P underlined that Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotionsfromsentences.Sentimentanalysisistheprocessusedtodeterminetheattitude/opinion/emotions expressed by a person about a specific topic based on Natural Language Processing (NLP). Proliferation of social media such as blogs, Twitter, Facebook and LinkedIn has fuelled interest in Sentiment analy- sis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassifications is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains. SECTION 2: COLLABORATIVE FILTERING: AN INTRODUCTION Chapter 4 by Venkatesan M and Dr. Thangadurai K analyzes the recommender systems, their history and its framework in brief. The current generation of filtering techniques in recommendation methods can be broadly classified into the following five categories. Techniques used in these categories are discussed in detail. Data mining algorithms techniques are implemented in recommender systems to filters user data ratings. Area of application of Recommender Systems gives broad idea and such as how it gives impact and why it is used in the e-commerce, Online Social Networks (OSN), and so on. It has shifted the core of Internet applications from devices to users. In this chapter, issues and recent research in recommender system are also discussed. In Chapter 5 by Neeti Sangwan and Naveen Dahiya urged that Recommendation making is an impor- tant part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommenda- tions to the users, efficient and cost effective methods needs to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests. Chapter 6 by Amrit Pal and Dr. Manish Kumar describes that Size of data is increasing; it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collabora- tive filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, map reduce based collaborative filtering are some of these techniques. Chapter addresses these techniques and some basics of collab- orative filtering In Chapter 7 by Anu Saini focused that today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behaviour. Recommendation systems are used to provide the recom- xx
Preface mendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering. SECTION 3: APPLICATIONS OF DATA MINING TECHNIQUES AND DATA ANALYSIS IN COLLABORATIVE FILTERING Arushi Jain, Dr. Vishal Bhatnagar and Pulkit Sharma in Chapter 8 canvass that there is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to a certain set of parameters than to those in other groups. These groups are known as clusters. Cluster analysis is one of the main tasks in the field of data mining and is a commonly used technique for statistical analysis of data. While big data collaborative filtering defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets such as viewpoints, multiple agents and pre-existing data about the users’ behaviour stored in matrices. Collaborative filtering is especially required when a huge data set is present. In chapter 9 Prof. Carson K. Leung, Fan Jiang, Edson M. Dela Cruz and Vijay Sekar Elango presents that Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valu- able to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering. Chapter 10 by Mahima Goyal and Dr. Vishal Bhatnagar discusses that the recent trend of express- ing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature review about the different tech- niques and applications of this field. The primary techniques can be classified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering. xxi
Preface Sheng-Jhe Ke and Wei-Po Lee in Chapter 11 emphasise that Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbours and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbours. In this chapter, we present a data analytics approach that combines user preference and social trust. This approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can enhance the recommendation performance in an adaptive way without an iterative parameter-tuning process. In Chapter 12 Dr. Marenglen Biba, Dr. Narasimha Rao Rao Vajjhala and Lediona Nishani provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter will begin with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collabora- tive filtering approaches will be presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. The chapter will also include the key section of the discussion on the latest trends in visual data mining for collaborative mining. Chapter 13 by Snehalata Sewakdas Dongre and Dr. Latesh Malik explored that A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly. Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play im- portant role here how the different classifiers work collaborative within the ensemble to achieve a goal. Lediona Nishani and Prof. Marenglen Biba in Chapter 14 presents that people nowadays base their behaviourbymakingchoicesthroughwordofmouth,media,publicopinion,surveys,etc.Oneofthemost prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filter- ing area and suggest further open issues in order to advance in this field of research. The applications of Collaborative Filtering Using Data Mining and Analysis are so vast that it cannot be covered in single book. However with the encouraging research contribution by the researchers in xxii
Preface this book, we (contributors) tried to sum the latest development and work in the area. This edited book will serve as the stepping stone and a factor of motivation for those young Researchers and Budding Engineers who are witnessing the every stopping growth in the field of Collaborative Filtering Using Data Mining and Analysis. Vishal Bhatnagar Ambedkar Institute of Advanced Communication Technologies and Research, India xxiii
 Acknowledgment  No work big or small is an accomplishment of any individual but a consistent and coordinated effort of a clique. Another driving force is the encouragement and guidance provided by friends and family, their words of motivation prove to be the guiding light in times of distress. I would like to express my heartfelt gratitude to all those, whose unremitting efforts helped me realize this piece of literature. First of all, I would like to thank my colleagues at the publishing team at Idea Group Publishing for their wonderful collaboration and timely reminder for the needful action at my end. You have sup- ported me incredibly throughout. In particular, I would like to single out the contributions of Courtney Tychinski whose continuous suggestions and valuable information kept me motivated also served as a timely reminder for the completion of the work. A journey of thousand miles begins with a single step, I would like to Kayla Wolfe for helping me with the inception of this book, I would also like to extend my gratitude to Jan Travers for timely completion of the contract agreement and helping me to finally decide the title of the book which I feel is essential for attracting the prospective contributors. I would like to thank all other support staff of Idea Group for extending their full support. A special word of mention to Professors and researcher’s across globe for agreeing to be part of the EAB and helping me to find a professor to write the Foreword. Thanks a ton Sir! Secondly, the editor would like to thank each one of the authors for their contributions. My sincere gratitude goes to the chapter’s authors who contributed their time and expertise to this book. I wish to thank the authors for their meticulous efforts and unparalleled perseverance that paved the way for the success of the project. I am also deeply indebted to all the reviewers, there deep insights regarding the improvement of quality, coherence, and content presentation of chapters were invaluable. Most of the authors also served as referees; I highly appreciate their double task their suggestions have contributed immensely to change the structure of the chapters and have transformed the book into its current form. Anyprojectcannotsucceedwithoutaconduciveenvironment.Iwouldliketotakethisopportunitytothank my colleagues at AIACTR for being the fulcrum of guidance motivation. I would like to thank Prof Ashok Mittal for his words of encouragement and invigoration. Also, I would like to thank my students Amit Kumar, Pulkit Sharma and Komal Mahajan for their help during the whole development process. At last, I would like to thank my parents for their blessing and words of wisdom, the values of hard work, passion and perseverance they inculcated in me as a child, have helped me sail through all the challenges I faced during the completion of this book. No words are enough to thank my beloved wife for standing xxiv
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Mr. Rankin. Had your husband said anything before or did he say anything at that time in regard to Mr. Nixon showing any hostility, friendship, or anything else? Mrs. Oswald. Showing any hostility or friendship toward Mr. Nixon? Mr. Rankin. Yes; toward Nixon. Mrs. Oswald. I don't remember him saying anything—I don't remember but he didn't tell me. I don't remember him saying anything of that sort. I only remember the next day he told me that Nixon did not come. Excuse me. Mr. Rankin. Yes. Mrs. Oswald. The FBI suggested that possibly I was confused between Johnson and Nixon but there is no question that in this incident it was a question of Mr. Nixon. I remember distinctly the name Nixon because I read from the presidential elections that there was a choice between President Kennedy and Mr. Nixon. Representative Ford. Where did your husband get the pistol that morning; do you remember? Mrs. Oswald. What, where? Representative Ford. Where. Mrs. Oswald. My husband had a small room where he kept all that sort of thing. It is a little larger than a closet. Representative Ford. Did you see him go in and get the pistol? Mrs. Oswald. I didn't see him go into the room. I only saw him standing before the open door and putting the pistol in his pocket. Representative Ford. Do you recall which pocket he put the pistol in? Mrs. Oswald. It was not in a pocket. He put it in his belt. (Discussion off the record.)
Mr. Dulles. Had you and your husband ever discussed Mr. Nixon at a previous, at any previous time? Mrs. Oswald. No. No. Mr. Rankin. What else happened about this incident beyond what you have told us? Mrs. Oswald. He took off his suit and stayed home all day reading a book. He gave me the pistol and I hid it under the mattress. Mr. Rankin. Did you say anything more than you have told us to him about this matter at that time? Mrs. Oswald. I closed the front door to the building that day and when we were quarreling about—when we were struggling over the question of whether or not he should go out I said a great deal to him. Mr. Rankin. What did you say to him then? Mrs. Oswald. I don't remember. Mr. Rankin. Just tell us in substance? Mrs. Oswald. I really don't remember now. I only remember that I told him that I am sorry of all these pranks of his and especially after the one with General Walker, and he had promised me, I told him that he had promised me—— Mr. Rankin. Did he say anything in answer to that? Mrs. Oswald. I don't remember. Mr. Dulles. As I recall, in your previous testimony there was some indication that you had said that if he did the Walker type of thing again you would notify the authorities. Did that conversation come up at this time with your husband? Mrs. Oswald. Yes; I said that. But he didn't go at that time and after all he was my husband.
Mr. Dulles. Does—do you mean you said it again at the time of the Nixon incident? Mrs. Oswald. Yes; I told him that but you must understand that I don't speak English very well, and for that reason I used to keep a piece of paper with me, and I had it, you know, what piece of paper I am talking about. At that time I didn't know how to go in police station: I don't know where it was. Mr. McKenzie. Was that the passport? Mrs. Oswald. No. After the incident with Walker—— Mr. Rankin. Was that paper the Walker incident note that you have described in your testimony? Mrs. Oswald. Yes. Representative Ford. When you put the pistol under the mattress, what happened to the pistol from then on? Mrs. Oswald. That evening he asked for it and said that nothing was going to happen, and that he said he wouldn't do anything and took the pistol back. And put it into his room. Mr. Dulles. Did you keep the, what you call, the Walker note with you all the time or did you have it in a particular place where you could go and get it and show it to him? Mrs. Oswald. I had it all the time. I kept it in a certain place initially and then I put it in the pages of a book. Senator Cooper. Mr. Rankin, would you ask the witness to state again what Lee Oswald's promise was to her that he had made at the time of the Walker incident? Mr. Rankin. Will you relate the promise that your husband made to you right after the discovery of the Walker incident by you? Mrs. Oswald. This wasn't a written promise. Mr. Rankin. No.
Mrs. Oswald. But in words it was more or less that I told him that he was very lucky that he hadn't killed—it very good that he hadn't killed General Walker. I said it was fate that—it was fated that General Walker not be killed and therefore he shouldn't try such a thing again. Mr. Rankin. What did he say in answer to that? Mrs. Oswald. He said perhaps I am right. I myself didn't believe what I was saying because I didn't believe that he was fated. I was just trying to find some way of dissuading my husband to do such a thing again. Do you understand what I mean? Mr. Rankin. Yes. Did he say that he would or would not do that again, that is what I want to know. Mrs. Oswald. At the time I did definitely convince him that I was right, and at the time he said that he would not do such a thing again. Mr. Rankin. Now, when you talked to him about the Nixon incident and persuaded him not to go out and do anything to Mr. Nixon, did you say anything about your pregnancy in trying to persuade him? Mrs. Oswald. Yes. Mr. Rankin. What did you say about that? Mrs. Oswald. Yes; I told him that I was pregnant. Mr. Rankin. Did you observe his action at the time of this Nixon incident, how he acted? Mrs. Oswald. How he reacted to this? Mr. Rankin. How he reacted to your interfering with him. Mrs. Oswald. At first he was extremely angry, and he said, You are always getting in my way. But then rather quickly he gave in, which was rather unusual for him. At the time I didn't give this any thought, but now I think it was just rather a kind of nasty joke he
was playing with me. Sometimes Lee was—he had a sadistic—my husband had a sadistic streak in him and he got pleasure out of harming people, and out of harming me, not physically but emotionally and mentally. Mr. Rankin. Have you told us substantially all that happened about this Nixon incident? Mrs. Oswald. That is all I can remember. Representative Ford. Can you tell us why you didn't mention this incident to the Commission when you appeared before? Mrs. Oswald. There were an awful lot of questions at that time, and I was very tired and felt that I had told everything and I don't remember, I can't understand why I didn't mention this. It would have been better for me to mention it the first time than to make you all do more work on it. Mr. Dulles. At the time of this incident did you threaten to go to the authorities in case your husband did not desist in his intention? Mrs. Oswald. Yes; I said that. Senator Cooper. I may have to go—could I ask a few questions? Mrs. Oswald, will you repeat what your husband said that morning when he dressed and got the pistol? Mrs. Oswald. I asked him where he was going and why he was getting dressed. He answered. Today Nixon is coming and I want to go out and have a look at him. I answered, I know how you look, and I had in mind the fact that he was taking a pistol with him. Senator Cooper. Did he say anything about what he intended to do with the pistol? Mrs. Oswald. No. Senator Cooper. Did you ask him if he intended to use the pistol against Mr. Nixon?
Mrs. Oswald. I told him that, You have already promised me not to play any more with that thing. Not really play, but, you know—I didn't mean, of course, just playing but using the pistol. Then he said, I am going to go out and find out if there will be an appropriate opportunity and if there is I will use the pistol. I just remembered this and maybe I didn't say this in my first testimony and now it just has occurred to me that he said this. Senator Cooper. Did your husband say why he wanted to use the pistol against Mr. Nixon? Mrs. Oswald. No. Senator Cooper. Did he say where he intended to see Mr. Nixon? Mrs. Oswald. He didn't say. He just said in Dallas, and since Nixon was coming to Dallas. Senator Cooper. When he was talking to you about seeing Mr. Nixon and using the pistol, what was his attitude? Was he angry or—— Mrs. Oswald. He wasn't angry. He looked more preoccupied and had sort of a concentrated look. Senator Cooper. Now, from the beginning, from the time that he first told you that he was going to use the pistol, until the time that you say he became quieted, did he again make any statement about using the pistol against Mr. Nixon? Mrs. Oswald. I told him that I didn't want him to use his gun any more. He said, I will go out and have a look and perhaps I won't use my gun, but if there is a convenient opportunity perhaps I will. Strike perhaps please from that last sentence. I didn't have a lot of time to think of what we were actually saying. All I was trying to do was to prevent him from going out. Senator Cooper. How much time elapsed, if you can remember, from the time he first told you that he was going out and when he finally became pacified?
Mrs. Oswald. This was maybe 30 minutes. The whole incident took maybe 20 minutes. It was about 10 minutes I took—15 minutes maybe. 15 minutes, it took maybe 10 minutes for him to be prepared to go out and then the incident in the bathroom took maybe 5 minutes until he quieted down. It doesn't mean I held him in the bathroom for 5 minutes because I couldn't do that but the general discussion in the bathroom. Senator Cooper. You said he stayed at the house the remainder of the day. During the remainder of the day did you discuss again with him the incident? Mrs. Oswald. No; no. Senator Cooper. Did he say anything more that day? Mrs. Oswald. No. He read a book. Mr. Dulles. Do you know what book it was, by chance? Mrs. Oswald. I don't remember. It was some kind of book from the public library. He had a two-volume history of the United States. This is not from the library, this was his own book. Mr. Dulles. The incident occurred, you said just a few days after he had told you he shot at General Walker? Mrs. Oswald. It was about 10 or 12 days after the incident with General Walker, perhaps about 3 days before we left for the departure for New Orleans. This didn't happen right after the incident with General Walker. It happened rather closer to a time when we departed for New Orleans. Mr. Dulles. The General Walker incident made a very strong impression on you, didn't it? Mrs. Oswald. Of course. I never thought that Lee had a gun in order to use it to shoot at somebody with. Mr. Dulles. Didn't this statement that he made about Vice President Nixon make a strong impression on you also?
Mrs. Oswald. I don't know. I was pregnant at the time. I had a lot of other things to worry about. I was getting pretty well tired of all of these escapades of his. Mr. Dulles. Was there any reason why you didn't tell the Commission about this when you testified before? Mrs. Oswald. I had no—there is no particular reason. I just forgot. Very likely this incident didn't make a very great impression on me at that time. Mr. Dulles. Now, before the death of President Kennedy, of course, you knew that your husband had purchased a rifle? Mrs. Oswald. Yes. Mr. Dulles. You knew that he had purchased a pistol? Mrs. Oswald. Yes. Mr. Dulles. And a knife? Mrs. Oswald. No; what kind of knife? Mr. Dulles. Did he have a knife? Mrs. Oswald. He had a little pocket knife; I think. Mr. Dulles. You knew that he had told you that he had tried to kill General Walker? Mrs. Oswald. Yes. Mr. Dulles. And, of course, as you said you heard him make a threat against Nixon. Mrs. Oswald. Yes. Mr. Dulles. Did you have some fear that he would use these weapons against someone else? Mrs. Oswald. Of course; I was afraid. Mr. Dulles. What? Mrs. Oswald. Of course; I was afraid.
Mr. Dulles. You thought that he might use his weapons against someone? Mrs. Oswald. After the incident with Nixon I stopped believing him. Mr. Dulles. You what? Mrs. Oswald. I stopped believing him. Mr. Dulles. Why? Mrs. Oswald. Because he wasn't obeying me any longer, because he promised and then he broke his promise. Mr. Dulles. Would you repeat that? Mrs. Oswald. Because he wasn't obeying me any more. He promised and, he made a promise and then he broke it. Mr. Dulles. That is my question. Having been told that—isn't it correct he told you that he shot at General Walker? He made a promise to you that he wouldn't do anything like that again, you heard him threaten Vice President Nixon, didn't it occur to you then that there was danger that he would use these weapons against someone else in the future? Mrs. Oswald. After the incident with Walker, I believed him when he told me that he wouldn't use the weapons any longer. Mr. Dulles. I remember you testified before and I asked you if you had heard him threaten any official or other person and your answer was no. Mrs. Oswald. Because I forgot at that time about the incident with Nixon. Mr. Dulles. I want to ask you again: In view of the fact that you knew—in view of the fact that he had threatened Walker by shooting at him, and he threatened Vice President Nixon can you not tell this Commission whether after that he threatened to hurt, harm any other person?
Mrs. Oswald. Nobody else. Perhaps I should be punished for not having said anything about all this, but I was just a wife and I was trying to keep the family together, at that time. I mean to say. I am talking, of course, of the time before President Kennedy's death. And if I forget to say anything now, I am not doing it on purpose. Mr. Dulles. I am just asking questions. Will you say here that he never did make any statement against President Kennedy? Mrs. Oswald. Never. Mr. Dulles. Did he ever make any statement about him of any kind? Mrs. Oswald. He used to read and translate articles from the newspaper about Kennedy to me and from magazines, favorable articles about Kennedy. He never commented on them and he never discussed them in any way but because of his translations and his reading to me he always had a favorable feeling about President Kennedy because he always read these favorably inclined articles to me. He never said that these articles never were true that he was a bad President or anything like that. Mr. Dulles. I didn't catch the last. Mrs. Oswald. He never said these articles were not true or that President Kennedy was a bad President or anything like that. Senator Cooper. I think you testified before that he made statements showing his dislike of our system of government and its economic system. Mrs. Oswald. He used to complain about the educational difficulties and about the unemployment in the United States and about the high cost of medical care. Mr. McKenzie. Right there, please, may I, Mr. Dulles when did he complain of those things, was this in Russia or was it in the United States after you returned from Russia?
Mrs. Oswald. After our return from Russia. When we were living in New Orleans after returning from Russia. Mr. McKenzie. Did he likewise make such complaints about the American system while you were living in Russia after you were married? Mrs. Oswald. He used to tell me that it was difficult to find a job and to get work in the United States but nonetheless we would be better there than we were in Russia. Excuse me. He was the kind of person who was never able to get along anywhere he was and when he was in Russia he used to say good things about the United States and when he was in the United States he used to talk well about Russia. Senator Cooper. You knew, of course, because of the incidents in New Orleans that he did not like American policy respecting Cuba. Mrs. Oswald. He was definitely a supporter of Cuba. This was something which remained with him from Russia. Senator Cooper. Did he ever say to you who was responsible or who had some responsibility for our policy toward Cuba? Mrs. Oswald. No. Senator Cooper. Had he ever mentioned President Kennedy in connection with our Cuban policy? Mrs. Oswald. Never to me. Mr. Dulles. Did he ever say anything—— Mrs. Oswald. He might have discussed this with Paine. Senator Cooper. With who? Mrs. Oswald. Mr. Paine, husband of Ruth Paine. Senator Cooper. He might have done what now? Mrs. Oswald. With the husband of Ruth Paine.
Senator Cooper. Why do you say that, did you ever hear him talking about it? Mrs. Oswald. He used to talk politics with Mr. Paine. I don't know what they were talking about because at that time I didn't understand English. Senator Cooper. Did you mean, though, to say that you believed he might have discussed the Cuban policy with Mr. Paine. Mrs. Oswald. Yes; especially after we returned from New Orleans. Senator Cooper. Why? Why do you make that statement? Mrs. Oswald. Because we only saw Mr. Paine once or twice before we went to New Orleans. And there was more opportunity to see Mr. Paine after we came back. Senator Cooper. But my question is what makes you think he might have talked to Mr. Paine about Cuba? Mrs. Oswald. I think, sir; because after returning from New Orleans this was his favorite subject, Cuba, and he was quite—a little bit cracked about it, crazy about Cuba. Senator Cooper. You mean he talked to you a great deal about it after you came from New Orleans? Mrs. Oswald. Well, in New Orleans he used to talk to me endlessly about Cuba, but after we came back he didn't talk to me about it any longer because I was just sick and tired of this. Mr. Dulles. He in this case is your husband? Mrs. Oswald. That is right. I really don't know about what he talked with Mr. Paine. I think that they were talking about politics, that is to say my husband with Mr. Paine because my husband used to tell me afterwards, Well, he doesn't understand anything about politics. He is not too strong on politics. And, therefore, I think they were probably talking with the American political system and the Russian political system and
comparisons between them. I think that Mr. Paine could probably tell you more about this than I can. Senator Cooper. That is all I want to ask for the time being. Mrs. Oswald. I think that Mr. Paine knows more about my husband's political attitudes toward the United States than I do. Mr. Rankin. You said the FBI asked you whether you could have been mistaken about it being Mr. Nixon that your husband was interested in going and seeing and maybe doing something to with his gun. Do you know what Mr. Johnson you were asking about? Let me rephrase the question. You said the FBI asked you whether you might have been mistaken about Mr. Nixon and whether it might have been Mr. Johnson instead of Mr. Nixon that your husband was interested in doing something to with his gun. Do you know what Mr. Johnson was being referred to? Mrs. Oswald. No; I didn't know who Johnson was. I am ashamed but I never knew his name. I am ashamed myself but I didn't know who Johnson was. Mr. Rankin. You didn't know that the FBI was asking about the then Vice President and now President Johnson? Mrs. Oswald. No; I never heard of Johnson before he became President. Mr. Dulles. And you are quite sure—— Mrs. Oswald. Maybe I am stupid, I don't know. Mr. Dulles. And you are quite sure that your husband mentioned the name of Nixon to you—— Mrs. Oswald. Yes; I am sure it was Nixon. Mr. Dulles. That morning?
Mr. Rankin. Do you know whether this Nixon incident occurred the day before your husband went to New Orleans? Mrs. Oswald. It wasn't the day before. Perhaps 3 days before. Mr. McKenzie. Mr. Rankin, may I ask a question? Mr. Rankin. Yes. Mr. McKenzie. Mrs. Oswald, you say or you said a few minutes ago that Mr. Paine knew or knows more about your husband's attitude about the United States than you do. Why did you say that? Mrs. Oswald. Because my husband's favorite topic of discussion was politics, and whoever he was with he talked to them politics and Mr. Paine was with him a fair amount and I am not sure they talked about politics. They went to meetings of some kind together, I don't know what kind of meetings. Mr. McKenzie. Do you know where the meetings were? Mrs. Oswald. In Dallas. After they came back from some meeting my husband said to me something about Walker being at this meeting, and he said, Paine knows that I shot him. I don't know whether this was the truth or not. I don't know whether it was true or not but this is what he told me. Mr. McKenzie. Would they go in Mr. Paine's automobile? Mrs. Oswald. Yes; it was about 2 days after this incident with Stevenson or the next day, or maybe it was the same place, or the next day that a meeting was held where General Walker appeared. Mr. McKenzie. It was the day before. Mrs. Oswald. The day before? The day after. I think there was 1 day's difference between them, either it was the day before or the day after. Mr. Rankin. Did you say that there were a number of political meetings——
Mrs. Oswald. Excuse me; but I think this was on Friday. I think that Lee was at this meeting on a Friday. Mr. Rankin. Did you say there were a number of political meetings that your husband went to—— Mrs. Oswald. Excuse me; this was October 24. Mr. Rankin. With Mr. Paine? Mrs. Oswald. A week after his birthday—this was Friday. I think it was a week after my husband's birthday about October 24 or something like that or the 25th. Mr. Rankin. Mr. Reporter, can you give her the question that I asked? Mrs. Oswald. Excuse me, please. (The question was read by the reporter.) Mrs. Oswald. I only know about this one. Mr. Rankin. Did the FBI tell you that the reason they were asking about whether there was a mistake as to whether it was Mr. Nixon or Vice President Johnson was because there was a report in Dallas papers about Vice President Johnson going to Dallas around the 23d of April? Mrs. Oswald. Yes; they did tell me this. They said that at this time there was only one announcement in the newspapers of anyone coming and that was Vice President Johnson. Mr. Rankin. But you still are certain it was Mr. Nixon and not Vice President Johnson? Mrs. Oswald. Yes, no. I am getting a little confused with so many questions. I was absolutely convinced it was Nixon and now after all these questions I wonder if I am right in my mind. Mr. Rankin. Did your husband—— Mrs. Oswald. I never heard about Johnson. I never heard about Johnson. I never knew anything about Johnson. I just don't think it
was Johnson. I didn't know his name. Mr. Rankin. Did you husband during the Nixon incident say Mr. Nixon's name several times or how many times. Mrs. Oswald. Only once. Mr. Rankin. Now, you said that your husband went to get the pistol in the room. Will you tell us what room that was that he went to get the pistol? Mrs. Oswald. It was a small sort of storeroom. Just to the left off the balcony as you come in; it is just on the left from the balcony. Mr. Rankin. Was it out, was the pistol out in the room or was it in a closet? Mrs. Oswald. This room contained only a table and some shelves, and the pistol was not on the table. It was hidden somewhere on a shelf. Representative Ford. Was the rifle in that room, too? Mrs. Oswald. Yes. Mr. Rankin. Where was the rifle in the room? Mrs. Oswald. Sometimes it was in the corner, sometimes it was up on a shelf. Lee didn't like me to go into this room. That is why he kept it closed all the time and told me not to go into it. Sometimes he went in there and sat by himself for long periods of time. Mr. Dulles. By closed, do you mean locked? Mrs. Oswald. He used to close it from the inside. I don't remember what kind of lock it was. Possibly it was just a—some kind of a tongue—— Mr. McKenzie. Latch. Mrs. Oswald. Latch or something like that. Mr. Dulles. How could he close it from the inside and then get out?
Mrs. Oswald. When he was inside he could close it from the inside so that I couldn't come in. Mr. Dulles. But when he came out could he close it from the outside so that you could not get in? Mrs. Oswald. No; from the outside it couldn't be locked. Representative Ford. When you went to New Orleans and packed for the trip to New Orleans, did you help to pack the pistol or the rifle? Mrs. Oswald. No, no; Lee never let me pack things when we went for trips. He always did it himself. Representative Ford. Did you see him pack the pistol or the rifle? Mrs. Oswald. No. Representative Ford. Did you know the pistol and the rifle were in the luggage going to New Orleans? Mrs. Oswald. I stayed for some time with Ruth Paine after he left for New Orleans and I don't know whether they were in his things or they were in the stuff which was left with me. Representative Ford. At the time Mrs. Paine picked you up to go to the bus station, did you intend to go by bus to New Orleans at that time? Mrs. Oswald. No. Representative Ford. While you were living on Neely Street you didn't tell us before of any extensive rifle shooting at Love Field or rifle practice at Love Field. Can you tell us more about it now? Mrs. Oswald. Lee didn't tell me when he was going out to practice. I only remember one time distinctly that he went out because he took the bus. I don't know if he went to Love Field at that time. I don't—after all this testimony, after all this testimony, when I was asked did he clean his gun a lot, and I answered yes, I
came to the conclusion that he was practicing with his gun because he was cleaning it afterwards. Representative Ford. Did he take the rifle and the pistol to Love Field or at the time he went on the bus? Mrs. Oswald. Only the rifle. Mr. McKenzie. Just a minute. Let me ask her a question. May I ask a question? Representative Ford. Yes, sir. Mr. McKenzie. Representative Ford, I wasn't here as you know when Mrs. Oswald testified before. I have been with her when she was interrogated by the FBI relative to practicing the rifle shooting. This is the first time that I have heard the use of the words Love Field. Has there been prior testimony by Mrs. Oswald here that he was practicing at Love Field, because the reason I ask this is because she has steadfastly in the past told me and the FBI that she didn't know where he went to practice and that is the reason I wanted to know. Mr. Rankin. The record is—— Mrs. Oswald. I don't know where he practiced. I just think that the bus goes to, went to Love Field. Mr. Rankin. Her testimony before was that the bus that he took, that she knows about when he went, was a bus that went to Love Field, and she thought he went to some place in that area to do his practicing. Mr. McKenzie. The reason I ask the question, Mr. Rankin, is because I don't believe there is any practice area at Love Field for rifle practicing. Mr. Rankin. Well, the investigation that the Commission has made shows that there is a place near Love Field where people do shooting and practicing. Mr. McKenzie. Not at Love Field.
Mr. Rankin. It is right adjacent, in the neighborhood. Mrs. Oswald. Once we went out with Kathy Ford with the children to watch airplanes landing and these airplanes made a tremendous noise and for that reason I thought that maybe my husband was practicing somewhere in that area because you couldn't hear the sound of shots. I don't know if there is any place near there where one can practice shooting, though. This idea just came to me a little while ago when we were out there, watching the airplanes because it was a couple of weeks ago that this happened. Just sort of a guess of mine. Mr. Dulles. How did he pack the gun or conceal the gun when he went out on the bus toward Love Field? Mrs. Oswald. Are you talking about the gun or the rifle? Mr. Dulles. I am talking about the rifle. Mrs. Oswald. He used to wrap it up in his overcoat, raincoat. Mr. Rankin. So that the record will be clear on this, Mr. McKenzie, the prior testimony did not purport to indicate that Mrs. Oswald thought he was practicing right on Love Field where the airplanes were landing or anything like that. It was that he took that bus and took the rifle and came back with the rifle and that the bus went to Love Field and the investigation has shown that there is at least one place in that immediate neighborhood where there is gun practice carried on. Mr. Dulles. Is there testimony, Mr. Rankin, as to more than one trip or should we get that from the witness? Mr. Rankin. She testified right now she only knew of this one although she knew of his cleaning his guns a number of times. She just testified to that. Do you want more than that? Mr. Dulles. I thought the record was a little fuzzy. Maybe you should clarify it. Mr. McKenzie. I think you should ask the question.
Mr. Rankin. Will you tell us, Mrs. Oswald, how you thought your husband might have been practicing in the area near Love Field or how you concluded that he might have been practicing with the rifle in the area near Love Field. Mrs. Oswald. Only because that is the bus, only because that is where the bus goes. He never told me where. Mr. Rankin. And you don't know whether he was practicing at a place near Love Field or some place between where he got on the bus near your home and Love Field; is that right? Mrs. Oswald. No; I don't know, even now I don't know where it is. Senator Cooper. Can I just ask a question? Do you know how many times he took the rifle from your home? Mrs. Oswald. Well—— Mr. Dulles. You are speaking of Neely Street. Mrs. Oswald. I only saw—— Senator Cooper. When you were living on Neely Street—strike that. You have told about his taking the rifle from the house on Neely Street and then later cleaning the rifle. Do you know how many times that occurred? Mrs. Oswald. I saw him take the rifle only once when we were living on Neely Street but he cleaned the rifle perhaps three or four times, perhaps three times—three times. Senator Cooper. Did he ever tell you that he was practicing with a rifle? Mrs. Oswald. Only after I saw him take the gun that one time. Senator Cooper. Did you ask him if he had been practicing with the rifle? Mrs. Oswald. Yes, I asked him. Senator Cooper. What did he say?
Mrs. Oswald. He said yes. Senator Cooper. Did he ever give any reason why he was practicing with the rifle to you? Mrs. Oswald. He didn't give me a reason. He just said that for a man it is an interesting thing to have a rifle. I considered this some kind of a sport for him. I didn't think he was planning to employ it. I didn't take it seriously. (At this point, Senator Cooper left the hearing room.) Mr. Rankin. At the time of the Nixon incident did you know who Mr. Nixon was? Mrs. Oswald. I didn't know what position he held. I thought he was Vice President. Mr. Rankin. Did you ever check to see whether Mr. Nixon was in fact in Dallas anytime around that date? Mrs. Oswald. No. Mr. Rankin. After the day of the Nixon incident did you ever discuss that incident again with your husband? Mrs. Oswald. No. Mr. Rankin. Did the Nixon incident have anything to do with your decision to go to New Orleans to live? Mrs. Oswald. After the incident with Walker it became clear to me that it would be a good idea to go away from Dallas and after the incident with Nixon insisted—I insisted on it. Mr. Rankin. After the Nixon incident did you ever discuss that Nixon incident again with your husband? Mrs. Oswald. No. I don't know why. Perhaps it didn't make a very strong impression on me and that is why I didn't mention it in my first testimony. Perhaps it is because the first incident with Walker made such a strong impression that what happened afterward was somewhat effaced by it. I was so much upset by this incident with
General Walker that I only just wanted to get away from Dallas as fast as possible. Mr. Rankin. Did you discuss the Nixon incident with anyone other than your husband before the assassination of President Kennedy? Mrs. Oswald. No. Mr. Rankin. Did you ever consider telling the police about the Walker and Nixon incidents? Mrs. Oswald. I thought of this but then Lee was the only person who was supporting me in the United States, you see. I didn't have any friends, I didn't speak any English and I couldn't work and I didn't know what would happen if they locked him up and I didn't know what would happen to us. Of course, my reason told me that I should do it but because of circumstances I couldn't do it. Mr. Rankin. When did you first tell something about the Nixon incident? Mrs. Oswald. It was after the assassination; we were in Martin's house and I think Robert was there also. That is when I first mentioned that. I don't remember whether I told them both at the same time or told Martin first and Robert second or Robert first and Martin second. Mr. Rankin. Do you know about when that was with reference to the time you moved in with the Martins? Mrs. Oswald. I think it was in the first month. I don't remember which day it was, though. Mr. Rankin. Do you recall whether you first told Robert about it some time in January of this year? Mrs. Oswald. I think it was earlier than that, early in December. Perhaps in the beginning of January, but I think it was before New Year's. Mr. Rankin. If Robert has stated that it was on a Sunday, January 12 of this year, do you think he is in error then?
Mrs. Oswald. I don't think that Robert would make a mistake. I might make a mistake myself but I don't think he would make a mistake because he doesn't have quite as many, because he has not been in contact with quite as many of these events and doesn't have quite as much to remember as I have. And in general, I have a bad memory for figures. Mr. Rankin. Did you discuss the Nixon incident at anytime with Mr. Thorne or Mr. Martin, your agent? Mrs. Oswald. I told Martin about it but I don't think I told Thorne about it, and if Thorne learned about it it must have been from Martin. Mr. Rankin. You just related how you told Mr. Martin about it and the occasion in your testimony a moment ago; is that right? Mrs. Oswald. I am certain that these were the circumstances in which I told Martin about this. Whether or not the—it's possible I was just talking with Martin and his wife about Lee and it just came into my mind and I don't remember whether Robert was there or not, or whether I told Robert later. Mr. Rankin. Did anyone at anytime advise you or tell you not to tell the Commission about this incident? Mrs. Oswald. Martin told me that it is not necessary to mention this. But when they were asking me here in the Commission whether I had anything to add to my testimony, I really forgot about it. When Martin and I were talking about it he said, Well, try not to think about these things too much. Mr. Rankin. Did he say anything about why it wasn't necessary to tell about this incident? Mrs. Oswald. I don't remember. I don't think he told me why. Maybe he told me and I just didn't understand because I didn't understand English very well. Mr. Rankin. When you were telling about the Nixon incident you referred to your husband's sadistic streak. Do you recall that?
Mrs. Oswald. Yes. Mr. Rankin. Can you tell us a little more about that, how it showed? Mrs. Oswald. Anytime I did something which didn't please him he would make me sit down at a table and write letters to the Russian Embassy stating that I wanted to go back to Russia. He liked to tease me and torment me in this way. He knew that this—he just liked to torment me and upset me and hurt me, and he used to do this especially if I interfered in any of his political affairs, in any of his political discussions. He made me several times write such letters. Mr. Dulles. I have just one question: What did you or your husband do with these letters that you wrote? Did any of them get mailed or did they all get destroyed? Mrs. Oswald. He kept carbons of these letters but he sent the letters off himself. Mr. Dulles. To the Russian Embassy? Mrs. Oswald. Yes; he didn't give me any money to buy stamps. I never had any pocket money of my own. Mr. Rankin. But the letters to the Embassy you are referring to are actual letters and requested—requests—they weren't practice letters or anything of that kind to punish you, were they? Mrs. Oswald. Yes; they were real letters. I mean if my husband didn't want me to live with him any longer and wanted me to go back, I would go back, not because I wanted to go back but I didn't have any choice. Mr. Rankin. I misunderstood you then because I thought you were describing the fact that he made you write letters as a part of this sadistic streak that would never be sent but what he actually did was have you prepare the letters and then he proceeded to send them, is that your testimony?
Mrs. Oswald. He did send them and he really wanted this. He knew that this hurt me. Mr. Rankin. Those are the letters to the Russian Embassy we have introduced in evidence in connection with your testimony; is that right? Mrs. Oswald. Yes; those are the letters. Representative Ford. Did he ever show you replies to those letters? Mrs. Oswald. At first—yes; there were. At first I didn't believe that he was sending off those letters. Representative Ford. But you did see the replies? Mrs. Oswald. I received answers from the embassy. Mr. Rankin. Now, I will turn to another subject, Mrs. Oswald. Mr. Dulles. Would you like to have a 5-minute recess? We will proceed. Mr. Rankin. Now, Mrs. Oswald, I would like to ask you about the Irving Gun Shop in Dallas. Mrs. Oswald. The what? I don't know anything about this at all. Mr. Rankin. Your counsel tells me I should correct that, that Irving is not a part of Dallas. It is the city of Irving. A witness has said that you and your two children and your husband came into a furniture shop asking the location of a gunshop in that area in Irving, and after appearing there that you and your husband, with your husband driving the car, along with your two children, got in the car and went up the street in the direction of where the gunshop was. Did you recall any incident of that kind? Mrs. Oswald. This is just a complete fabrication. Lee never drove a car with me. Only Ruth Paine drove a car with me. And I never took my baby with me. Mr. Rankin. Did you ever go into such a furniture store in Irving?
Mrs. Oswald. Never. Mr. Rankin. That you recall? Mrs. Oswald. I was only twice in a store in Irving where they sell, like a cafe, where you can buy something to eat and where they sell toys and clothes and things like that; a little bit like a Woolworths, a one-story shop but without any furniture in it. Mr. Rankin. Do you know a Mrs. Whitworth who works in a furniture store in Irving? Mrs. Oswald. I was never in Irving in any furniture store. Mr. Rankin. Do you know a Mrs. Whitworth? Mrs. Oswald. It is the first time I have ever heard that name. Mr. Rankin. Do you know a Mrs. Hunter, a friend of Mrs. Whitworth? Mrs. Oswald. No. Mr. Rankin. Did you ever go on a trip with your husband to have a telescopic lens mounted on a gun at a gunshop? Mrs. Oswald. Never. No; this is all not true. In the first place, my husband couldn't drive, and I was never alone with him in a car. Anytime we went in a car it was with Ruth Paine, and there was never—we never went to any gun store and never had any telescopic lens mounted. Mr. Rankin. Did the four of you, that is, your husband, you, and your two children, ever go alone any place in Irving? Mrs. Oswald. In Irving the baby was only 1 month old. I never took her out anywhere. Representative Ford. Did you ever go anytime—— Mrs. Oswald. Just to doctor, you know. Representative Ford. Did you ever go anytime with your husband in a car with the rifle?
Mrs. Oswald. I was never at anytime in a car with my husband and with a rifle. Not only with the rifle, not even with a pistol. Even without anything I was never with my husband in a car under circumstances where he was driving a car. Representative Ford. Did you go in a car with somebody else driving where your husband had the pistol or the rifle? Mrs. Oswald. Never. I don't know what to think about this. Mr. Rankin. Mrs. Oswald, I will hand you Commission's Exhibit No. 819 and ask you particularly about the signature at the bottom. Mrs. Oswald. That is Lee's handwriting, and this is mine. Mr. Rankin. Were the words A. J. Hidell, Chapter President on Commission Exhibit No. 819 are in your handwriting? Mrs. Oswald. Yes. Mr. Rankin. Would you tell the Commission how you happened to sign that? Mrs. Oswald. Lee wrote this down on a piece of paper and told me to sign it on this card, and said that he would beat me if I didn't sign that name on the card. Mr. Rankin. Did you have any other discussion about your signing that name? Mrs. Oswald. Yes. Mr. Rankin. What discussion did you have? Mrs. Oswald. I said that this sounded like Fidel. I said, You have selected this name because it sounds like Fidel and he blushed and said, Shut up, it is none of your business. Mr. Rankin. Was there any discussion about who Hidell, as signed on the bottom of that card, was? Mrs. Oswald. He said that it was his own name and a there is no Hidell in existence, and I asked him, You just have two names, and he said, Yes.
Mr. Rankin. Was anything else said about that matter at any time? Mrs. Oswald. I taunted him about this and teased about this and said how shameful it is that a person who has his own perfectly good name should take another name and he said, It is none of your business, I would have to do it this way, people will think I have a big organization and so forth. Mr. Rankin. Did you ask him why he needed to have the other name in your handwriting rather than his own? Mrs. Oswald. I did ask him that and he would answer that in order that people will think it is two people involved and not just one. Mr. Dulles. Did you ever sign any more such cards with the name Hidell? Mrs. Oswald. Only this one. Mr. Dulles. And you never signed the name Hidell on any other paper at any time? Mrs. Oswald. Only once. Representative Ford. Where did this actual signing take place, Mrs. Oswald? Mrs. Oswald. In New Orleans. Representative Ford. Where in New Orleans? Mrs. Oswald. In what is the name of the street where we lived, in an apartment house. Representative Ford. In your apartment house? Mrs. Oswald. Yes; in our apartment house. Representative Ford. What time of day, do you recall? Mrs. Oswald. It might have been 8 or 9 o'clock in the evening. Mr. Dulles. Had you ever heard the name Hidell before?
Mrs. Oswald. I don't remember whether this was before or after Lee spoke on the radio. I think it was after. Mr. Dulles. Did he use the name Hidell on the radio? Mrs. Oswald. I think that he might have when he was talking on the radio said that Hidell is the President of his organization but, of course, I don't understand English well and I don't know. He spoke on the radio using his own name but might have mentioned the name Hidell. This is what he told me. When I tried to find out what he said on the radio. Mr. Dulles. This might have been on television also? Mrs. Oswald. It was on the radio, not on television. He told me that someone had taken movies of him for to be shown later on television but I don't know if they ever were. Mr. Dulles. Did you ever sign the name Hidell at any subsequent time to any document? Mr. McKenzie. If you recall signing it. Do you recall signing his name to any other document? Mrs. Oswald. I only remember this one occasion. Mr. Rankin. Was the way you signed on this Commission's Exhibit No. 819 your usual way of writing English? Mrs. Oswald. My English handwriting changes every day, and my Russian handwriting, too. But that is more or less my usual style. Mr. Rankin. You weren't trying to conceal the way you sign anything? Mrs. Oswald. I tried to do it, I just tried to write it as nicely as possible. Mr. Dulles. Did you make some practice runs of writing this name before you actually put it on the card? Mrs. Oswald. Yes; because it was difficult for me to write English properly.
Mr. Dulles. So you mean you wrote it several times on another sheet of paper and then put it on this card? Mrs. Oswald. Yes. Representative Ford. Was there anybody else present at the time of this incident? Mrs. Oswald. No; only Lee. Representative Ford. Did he have you sign only one card? Mrs. Oswald. This was the only time when I—when Lee asked me to do this and I did it. I might have signed two or—cards and not just one but there weren't a great many. Representative Ford. Did the other cards have someone else's name besides Lee Harvey Oswald on it? Mrs. Oswald. No; only Lee Oswald. Representative Ford. But you think you might have signed more than one such card? Mrs. Oswald. Maybe two, three. This is just 1 day when I was signing this. It just happened on one occasion. Mr. Rankin. Mrs. Oswald, turning to another subject, I would like to ask you about some correspondence with the Dallas Civil Liberties Union. Do you recall that they inquired as to whether you were being kept from seeing and speaking to people against your will? Mrs. Oswald. This letter was translated by Ruth Paine and I answered on the basis of the translation. Mr. McKenzie. May I see those letters, Mr. Rankin? Mr. Rankin. Yes. Mrs. Oswald. I didn't want to answer this letter. It was simply a matter of courtesy on my part.
Mr. Rankin. Now, you received a letter from the local chapter of the Civil Liberties union in Russian, did you not? Mrs. Oswald. There was a letter that was in English and there was a translation which came with it, and it was stated that the translation was done by Ruth Paine. Mr. Rankin. What did you do with the translation or the—I will ask you the translation first. Did you keep that? Mrs. Oswald. I don't remember what I did with it. Mr. Rankin. Do you know what you did with the part that was in Russian? Mrs. Oswald. Perhaps it is somewhere among my papers but I didn't pay any special attention to it. Mr. Rankin. I will hand you Commission Exhibit No. 331 and ask you if that is the letter in English that you referred to? Mrs. Oswald. Yes; it is the letter. Mr. Rankin. I call the Commission's attention to the fact that that has already been received in evidence. Mr. McKenzie. Mr. Rankin, did you write Mr. Olds about this? This appears to be a letter in reply to a letter from you. Mr. Rankin. That is right. I asked for it. Mrs. Oswald, will you examine Commission Exhibits Nos. 990 and 991 and state whether you know the handwriting in these exhibits? Mrs. Oswald. This is all mine, my handwriting. This is the answer to that letter. Mr. Rankin. And the letter, Exhibit No. 990, and the envelope, Exhibit No. 991, in your handwriting were your response to the inquiry of the Dallas Civil Liberties Union on the Exhibit No. 331? Mrs. Oswald. Yes; this was my answer to this letter, Exhibit No. 331.
Mr. Rankin. I offer in evidence Commission Exhibits Nos. 990 and 991. Mr. Dulles. You want them admitted at this time? Mr. Rankin. Yes; Mr. Chairman. Mr. Dulles. They shall be admitted. (Commission Exhibits Nos. 990 and 991 were marked for identification and received in evidence.) Mr. Rankin. Mrs. Oswald, I will ask you to examine Exhibit No. 988 and with the help of the interpreter, advise us whether or not it is a reasonably correct translation of your letter, Exhibit No. 990. Mrs. Oswald. This is not an accurate translation. Mr. Rankin. Mrs. Oswald, can you tell us what errors were made, where the corrections should be to make it a correct translation? Mrs. Oswald. There is one place here in which it refers to the third sentence of the English text which states: What you read in the papers is correct. Mr. Rankin. How would you correct that? Mrs. Oswald. This is incorrect. A better, a proper translation, although unofficial of this passage, and the Russian text of my letter would read, Your concern is quite unnecessary although it is quite understandable if one is to judge from what is written in the papers. Mr. Rankin. Now, will you proceed with any other corrections? Mrs. Oswald. This, the letter, the spirit of the letter reflects my own spirit in my own Russian text—although the translation is somewhat inaccurate and tends to shorten my own text somewhat. There is another inaccuracy which is more important than the others—it is not more important, the first one is more important— there is another which should be called to the Commission's attention.
The last sentence of the English text reads: Please let Mrs. Ruth Paine know I owe to her much and think of her as one of my best friends. Whereas the letter only states that: Of course, consider her my friend. Mr. Rankin. Mrs. Oswald, I call your attention to Commission Exhibit No. 990 and ask you to note the date which appears to be December 7, 1964. The Dallas Civil Liberties Union letter, you will note, was dated January 6, 1964 which I will hand you so you can examine it. Could you explain that discrepancy? You might wish to examine them. Mrs. Oswald. It can't possibly be the 7th of December 1964 because it hasn't even come yet. Mr. Rankin. You might wish to examine the envelope, Exhibit No. 991, that may help you as to the correct date. Mrs. Oswald. January 8. I wrote this January 7. It was just my mistake. I wrote it on January 7 and mailed it on the 8th. I just out of habit still writing December. Mr. McKenzie. Mr. Rankin, may I ask the Commission, on Commission Exhibit No. 988, which purports to be a translation of Mrs. Oswald's letter to the Dallas Civil Liberties Union, do you know who translated this letter or could you tell us who translated the letter? Mr. Rankin. Mr. McKenzie—— Mrs. Oswald. They wrote me that I can answer them in Russian, and which I did but I haven't any idea who translated my answer. Mr. Rankin. The Commission Exhibit No. 987 which I will now offer states that the translation was handled by Mrs. Ford and later seen by Mrs. Paine. The translation of the exhibit that you now have in your hand, what is the number of that?
Mr. McKenzie. This is Commission Exhibit No. 988 in English which purports to be a translation of Mrs. Oswald's letter to the Dallas Civil Liberties Union and I am asking does the Commission know who translated the letter? Mr. Rankin. We were informed by the Dallas Civil Liberties Union in Exhibit No. 987 that the translation was made by Mrs. Ford and later seen by Mrs. Paine, and I now offer all exhibits together with Exhibit No. 987 as part of the testimony of this witness. Mr. Dulles. The exhibits shall be admitted. Have we the numbers of all of these exhibits? Mr. Rankin. Yes; the reporter has them. (Commission Exhibit No. 987 was marked for identification and received in evidence.) Mr. Rankin. Mrs. Oswald, I will hand you the cameras of your—— Mr. Dulles. I wonder before we finish this—— Mr. McKenzie. I would prefer, Mr. Rankin, for the purposes of the record so that the record will be complete, to have a correct English translation of Mrs. Oswald's letter in the record in lieu of Commission Exhibit No. 988. Mr. Rankin. Mr. Chairman, if it is agreeable to the Commission, I would like to ask counsel to furnish such a translation and we will then make it the next number, Exhibit No. 992, as a part of this record. Mr. Dulles. That shall be admitted then as Exhibit No. 992, the other already being in the record I think, probably has to stay there particularly in view of all this discussion of it. Mr. Rankin. If you will furnish it. Mr. McKenzie. You are putting the onus or burden back on me, Mr. Rankin, when the Commission has a fully qualified, I presume, Russian interpreter here, and if the Commission would not mind
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  • 6. Collaborative Filtering Using Data Mining and Analysis Vishal Bhatnagar Ambedkar Institute of Advanced Communication Technologies and Research, India A volume in the Advances in Data Mining and Database Management (ADMDM) Book Series
  • 7. Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2017 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: eresources@igi-global.com. Names: Bhatnagar, Vishal, 1977- author. Title: Collaborative filtering using data mining and analysis / Vishal Bhatnagar, editor. Description: Hershey, PA : Information Science Reference, [2017] | Includes bibliographical references and index. Identifiers: LCCN 2016012755| ISBN 9781522504894 (hardcover) | ISBN 9781522504900 (ebook) Subjects: LCSH: Data mining. | Recommender systems (Information filtering) | Multiagent systems. Classification: LCC QA76.9.D343 C747 2017 | DDC 006.3/12--dc23 LC record available at https://lccn.loc.gov/2016012755 This book is published in the IGI Global book series Advances in Data Mining and Database Management (ADMDM) (ISSN: 2327-1981; eISSN: 2327-199X)
  • 8. The Advances in Data Mining and Database Management (ADMDM) Book Series (ISSN 2327-1981) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http:// www.igi-global.com/book-series/advances-data-mining-database-management/37146. Postmaster: Send all address changes to above address. Copyright © 2017 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or informa- tion and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global. IGI Global is currently accepting manuscripts for publication within this series. To submit a pro- posal for a volume in this series, please contact our AcquisitionEditorsatAcquisitions@igi-global.com or visit: http://www.igi-global.com/publish/. • Data warehousing • Sequence Analysis • Quantitative Structure–Activity Relationship • Profiling Practices • Data Analysis • Neural Networks • Database Testing • Cluster Analysis • Web-based information systems • Information Extraction Coverage With the large amounts of information available to organizations in today’s digital world, there is a need forcontinualresearchsurroundingemergingmethodsandtoolsforcollecting,analyzing,andstoringdata. The Advances in Data Mining Database Management (ADMDM) series aims to bring together research in information retrieval, data analysis, data warehousing, and related areas in order to become an ideal resource for those working and studying in these fields. IT professionals, software engineers, academicians and upper-level students will find titles within the ADMDM book series particularly use- ful for staying up-to-date on emerging research, theories, and applications in the fields of data mining and database management. Mission ISSN: 2327-1981 EISSN: 2327-199X David Taniar Monash University, Australia Advances in Data Mining and Database Management (ADMDM) Book Series
  • 9. Titles in this Series For a list of additional titles in this series, please visit: www.igi-global.com Intelligent Techniques for Data Analysis in Diverse Settings Numan Celebi (Sakarya University, Turkey) Information Science Reference • copyright 2016 • 353pp • H/C (ISBN: 9781522500759) • US $195.00 (our price) Managing and Processing Big Data in Cloud Computing Rajkumar Kannan (King Faisal University, Saudi Arabia) Raihan Ur Rasool (King Faisal University, Saudi Arabia) Hai Jin (Huazhong University of Science and Technology, China) and S.R. Balasundaram (National Institute of Technology, Tiruchirappalli, India) Information Science Reference • copyright 2016 • 307pp • H/C (ISBN: 9781466697676) • US $200.00 (our price) Handbook of Research on Innovative Database Query Processing Techniques Li Yan (Nanjing University of Aeronautics and Astronautics, China) Information Science Reference • copyright 2016 • 625pp • H/C (ISBN: 9781466687677) • US $335.00 (our price) Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence Noor Zaman (King Faisal University, Saudi Arabia) Mohamed Elhassan Seliaman (King Faisal University, Saudi Arabia) Mohd Fadzil Hassan (Universiti Teknologi PETRONAS, Malaysia) and Fausto Pedro Garcia Marquez (Campus Universitario s/n ETSII of Ciudad Real, Spain) Information Science Reference • copyright 2015 • 500pp • H/C (ISBN: 9781466685055) • US $285.00 (our price) Improving Knowledge Discovery through the Integration of Data Mining Techniques Muhammad Usman (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Pakistan) Information Science Reference • copyright 2015 • 391pp • H/C (ISBN: 9781466685130) • US $225.00 (our price) Modern Computational Models of Semantic Discovery in Natural Language Jan Žižka (Mendel University in Brno, Czech Republic) and František Dařena (Mendel University in Brno, Czech Republic) Information Science Reference • copyright 2015 • 335pp • H/C (ISBN: 9781466686908) • US $215.00 (our price) Mobile Technologies for Activity-Travel Data Collection and Analysis Soora Rasouli (Eindhoven University of Technology, The Netherlands) and Harry Timmermans (Eindhoven Uni- versity of Technology, The Netherlands) Information Science Reference • copyright 2014 • 325pp • H/C (ISBN: 9781466661707) • US $225.00 (our price) 701 E. Chocolate Ave., Hershey, PA 17033 Order online at www.igi-global.com or call 717-533-8845 x100 To place a standing order for titles released in this series, contact: cust@igi-global.com Mon-Fri 8:00 am - 5:00 pm (est) or fax 24 hours a day 717-533-8661
  • 10.    Editorial Advisory Board Manish Kumar, IIIT Allahabad, India Rakesh E. M., IBM India, India Rashmi Malhotra, Saint Joseph’s University, USA Latesh Malik, G.H. Raisoni College of Engineering, India Tarun Srivastava, Deloitte India, India Golla Varaprasad, BMS College of Engineering, India Toyohide Watanabe, Nagoya Industrial Science Research Institute, Japan Jie Yan, Stanford University, USA Yudong Zhang, Columbia University, USA New York State Psychiatric Institute, USA
  • 11.  Table of Contents  Foreword............................................................................................................................................... xv Preface. .................................................................................................................................................xvi Acknowledgment...............................................................................................................................xxiv Section 1 Data Mining Techniques and Analysis: An Overview Chapter 1 Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System.................................................................................................................................. 1 Renuka Mahajan, Amity University UP, India Chapter 2 Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value......... 24 Mamta Mittal, G. B. Pant Govt. Engineering College, India R. K. Sharma, Thapar University, India V.P. Singh, Thapar University, India Lalit Mohan Goyal, Bharati Vidyapeeth College of Enineering, India Chapter 3 Dimensionality Reduction Techniques for Text Mining........................................................................ 49 Neethu Akkarapatty, SCMS School of Engineering and Technology, India Anjaly Muralidharan, SCMS School of Engineering and Technology, India Nisha S. Raj, SCMS School of Engineering and Technology, India Vinod P., SCMS School of Engineering and Technology, India Section 2 Collaborative Filtering: An Introduction Chapter 4 History and Overview of the Recommender Systems........................................................................... 74 Venkatesan M., Government Arts College (Autonomous), India Thangadurai K., Government Arts College (Autonomous), India
  • 12.  Chapter 5 A Classification Framework Towards Application of Data Mining in Collaborative Filtering........... 100 Neeti Sangwan, Maharaja Surajmal Institute of Technology, India Naveen Dahiya, Maharaja Surajmal Institute of Technology, India Chapter 6 Collaborative Filtering Based Data Mining for Large Data. ................................................................ 115 Amrit Pal, Indian Institute of Information Technology Allahabad, India Manish Kumar, Indian Institute of Information Technology Allahabad, India Chapter 7 Big Data Mining Using Collaborative Filtering.................................................................................. 128 Anu Saini, G.B. Pant Engineering College, India Section 3 Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering Chapter 8 Collaborative and Clustering Based Strategy in Big Data................................................................... 140 Arushi Jain, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Pulkit Sharma, Ambedkar Institute of Advanced Communication Technologies and Research, India Chapter 9 Association Rule Mining in Collaborative Filtering............................................................................ 159 Carson K. Leung, University of Manitoba, Canada Fan Jiang, University of Manitoba, Canada Edson M. Dela Cruz, University of Manitoba, Canada Vijay Sekar Elango, University of Manitoba, Canada Chapter 10 A Classification Framework on Opinion Mining for Effective Recommendation Systems................ 180 Mahima Goyal, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Chapter 11 Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach. ............................................................................................................................. 195 Sheng-Jhe Ke, National Sun Yat-Sen University, Taiwan Wei-Po Lee, National Sun Yat-Sen University, Taiwan
  • 13.  Chapter 12 Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey....................................... 217 Marenglen Biba, University of New York Tirana, Albania Narasimha Rao Vajjhala, University of New York Tirana, Albania Lediona Nishani, University of New York Tirana, Albania Chapter 13 Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers............... 236 Snehlata Sewakdas Dongre, Ghrce Nagpur, India Latesh G. Malik, Ghrce Nagpur, India Chapter 14 Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review....................... 250 Lediona Nishani, University of New York Tirana, Albania Marenglen Biba, University of New York in Tirana, Albania Compilation of References................................................................................................................ 270 About the Contributors..................................................................................................................... 302 Index.................................................................................................................................................... 307
  • 14.   Detailed Table of Contents  Foreword............................................................................................................................................... xv Preface. .................................................................................................................................................xvi Acknowledgment...............................................................................................................................xxiv Section 1 Data Mining Techniques and Analysis: An Overview Chapter 1 Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System.................................................................................................................................. 1 Renuka Mahajan, Amity University UP, India This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what’ can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what’ can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning. Chapter 2 Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value......... 24 Mamta Mittal, G. B. Pant Govt. Engineering College, India R. K. Sharma, Thapar University, India V.P. Singh, Thapar University, India Lalit Mohan Goyal, Bharati Vidyapeeth College of Enineering, India Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but
  • 15.  they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets. Chapter 3 Dimensionality Reduction Techniques for Text Mining........................................................................ 49 Neethu Akkarapatty, SCMS School of Engineering and Technology, India Anjaly Muralidharan, SCMS School of Engineering and Technology, India Nisha S. Raj, SCMS School of Engineering and Technology, India Vinod P., SCMS School of Engineering and Technology, India Sentimentanalysisisanemergingfield,concernedwiththeanalysisandunderstandingofhumanemotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains. Section 2 Collaborative Filtering: An Introduction Chapter 4 History and Overview of the Recommender Systems........................................................................... 74 Venkatesan M., Government Arts College (Autonomous), India Thangadurai K., Government Arts College (Autonomous), India This Chapter analyzes the recommender systems, their history and its framework in brief. The current generationoffilteringtechniquesinrecommendationmethodscanbebroadlyclassifiedintothefollowing five categories. Techniques used in these categories are discussed in detail. Data mining algorithms techniques are implemented in recommender systems to filters user data ratings. Area of application of Recommender Systems gives broad idea and such as how it gives impact and why it is used in the e-commerce, Online Social Networks (OSN), and so on. It has shifted the core of Internet applications fromdevicestousers.Inthischapter,issuesandrecentresearchinrecommendersystemarealsodiscussed. Chapter 5 A Classification Framework Towards Application of Data Mining in Collaborative Filtering........... 100 Neeti Sangwan, Maharaja Surajmal Institute of Technology, India Naveen Dahiya, Maharaja Surajmal Institute of Technology, India Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommendations to the users, efficient and cost effective methods needs
  • 16.  to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests. Chapter 6 Collaborative Filtering Based Data Mining for Large Data. ................................................................ 115 Amrit Pal, Indian Institute of Information Technology Allahabad, India Manish Kumar, Indian Institute of Information Technology Allahabad, India Size of data is increasing, it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collaborative filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, mapreducebasedcollaborativefilteringaresomeofthesetechniques.Chapteraddressesthesetechniques and some basics of collaborative filtering. Chapter 7 Big Data Mining Using Collaborative Filtering.................................................................................. 128 Anu Saini, G.B. Pant Engineering College, India Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering. Section 3 Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering Chapter 8 Collaborative and Clustering Based Strategy in Big Data................................................................... 140 Arushi Jain, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India Pulkit Sharma, Ambedkar Institute of Advanced Communication Technologies and Research, India There is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to
  • 17.  a certain set of parameters than to those in other groups. These groups are known as clusters. Cluster analysisisoneofthe maintasksin the field ofdatamining and isacommonly usedtechniqueforstatistical analysis of data. While big data collaborative filtering defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets such as viewpoints, multiple agents and pre-existing data about the users’ behavior stored in matrices. Collaborative filtering is especially required when a huge data set is present. Chapter 9 Association Rule Mining in Collaborative Filtering............................................................................ 159 Carson K. Leung, University of Manitoba, Canada Fan Jiang, University of Manitoba, Canada Edson M. Dela Cruz, University of Manitoba, Canada Vijay Sekar Elango, University of Manitoba, Canada Collaborativefilteringusesdataminingandanalysistodevelopasystemthathelpsusersmakeappropriate decisionsinreal-lifeapplicationsbyremovingredundantinformationandprovidingvaluabletoinformation users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF- Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering. Chapter 10 A Classification Framework on Opinion Mining for Effective Recommendation Systems................ 180 Mahima Goyal, Ambedkar Institute of Advanced Communication Technologies and Research, India Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies and Research, India With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature reviewaboutthedifferenttechniquesandapplicationsofthisfield.Theprimarytechniquescanbeclassified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering
  • 18.  Chapter 11 Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach. ............................................................................................................................. 195 Sheng-Jhe Ke, National Sun Yat-Sen University, Taiwan Wei-Po Lee, National Sun Yat-Sen University, Taiwan Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbors for a particular user and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbors. In this chapter, we present a data analytics approach that combines user preference and social trust for making better collaborative recommendation. The proposed approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can be used to enhance the recommendation performance in an adaptive way for different datasets without an iterative parameter-tuning process. Chapter 12 Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey....................................... 217 Marenglen Biba, University of New York Tirana, Albania Narasimha Rao Vajjhala, University of New York Tirana, Albania Lediona Nishani, University of New York Tirana, Albania Thisbookchapterprovidesastate-of-the-artsurveyofvisualdataminingtechniquesusedforcollaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysisofthestate-of-the-artvisual dataminingtechniquesusedby researchersaswellasintheindustry. Collaborativefilteringapproachesarepresentedalongwithananalysisofthestate-of-the-artcollaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering. Chapter 13 Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers............... 236 Snehlata Sewakdas Dongre, Ghrce Nagpur, India Latesh G. Malik, Ghrce Nagpur, India A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly.
  • 19.  Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play important role here how the different classifiers work collaborative within the ensemble to achieve a goal. Chapter 14 Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review....................... 250 Lediona Nishani, University of New York Tirana, Albania Marenglen Biba, University of New York in Tirana, Albania People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research. Compilation of References................................................................................................................ 270 About the Contributors..................................................................................................................... 302 Index.................................................................................................................................................... 307
  • 20.   Foreword  The book entitled Collaborative Filtering Using Data Mining and Analysis comes as a timely and badly needed volume covering the most recent developments in this rapidly growing area. Even a quick query on Google Scholar when searching for “recommender system” shows a highly convincing pattern: the number of hits in 2000 was 489, in 2005 went up to 2,310 and in 2015 reached 9,900. The area is growing rapidly and calls for new methodologies, innovative ways of thinking and efficient solutions, especially when coping with new areas of applications. Recommender systems grow not only in their number but alsointhelevelofsophisticationandengagementofadvancedinformationprocessingtechnologiesinclud- ing active involvement of Computational Intelligence, sentiment analysis, and text analysis, in general. The book is organized in the three main parts being focused on data mining, collaborative filtering, and applications in collaborative filtering. The first part covers some review material on data mining, clustering, and dimensionality reduction. Definitely, this type of material is highly pertinent to the volume, given the evolution of the area of recommender systems where we have been witnessing a well-motivated quest for advanced techniques of data analysis, especially those falling under the rubric of data mining and becoming indispensable when coping with heterogeneous sources of data includ- ing textual ones. The second part of the book is aimed at the presentation of material on collaborative filtering. Collaborating filtering is inherently linked with recommender systems and constitutes an area of numerous and intensive research endeavors. The four chapters forming this part are a good reflection of the current tendencies. In addition to some historical exposure of the topic, discussed is a certain classification framework showing a role of data mining in collaborative filtering. The challenge posed by the emergence of big data is elaborated in the two last chapters of this part. The third part of the volume looks at various application facets, including studies on association rules, clustering, big data, data streams, all present in the setting of collaborative filtering. The volume delivers a coherent, logically arranged, and up-to-date material addressing various facets of collaborative filtering and recommender systems. The Editor and the authors deserve thanks for producing a timely, well-structured, and insightful volume, which will appeal to a broad readership. Not only those interested in recommender systems could find this book useful but the material could be of tangible value to researchers in data mining, sentiment analysis, and decision-making. Witold Pedrycz University of Alberta, Canada xv
  • 21.  Preface  Recommendersystemshavedevelopedinparallelwiththeweb.Withthedevelopmentofweb,theinforma- tion available online increased at an exponential rate. This information overload required a system which could remove redundant information and provide the most valuable information to a user in minimum time. Collaborative Filtering is one the most accurate and widely adopted approaches for providing such information. It has found its application in domains ranging from e-commerce and e-learning to social networks and web search. Owing to its vast field, techniques, and challenges pertaining to collaborative filtering requires it to be conglomerated at one place to understand its underlying principle, working and application in its entirety. Collaborative filtering finds its roots in data-mining. Data mining is finding hidden and unknown information from inside large databases. Data mining tools and techniques are finding its immense applications in the modern day. Collaborative filtering using data mining will widen the application area and more interest will be created in budding research- ers to pursue their research in the same. The implications of data mining can be understood by the fact that whether it’s a public or private sector organization, all are taking the advantage of the data mining tools and techniques to reveal the hidden and unknown information from the available data. This has been widened primarily because of the large or can we say terabyte of data which is collected by all the organizations over the year and they are confused as how to use such a bulk of data. The new and emerging areas of data mining techniques have surprised many researchers and business persons who are gaining a lot of hidden and unknown information for increasing their ROI. Collaborative Filtering is one the most accurate and widely-adopted approaches for providing such information. It has found its application in domains ranging from e-commerce and e-learning to social networks and web search. The primarily techniques of data mining are: 1. Classification: A supervised learning-based technique in which different items are classified into target classes. This technique is used in the cases where the exact prediction is required. In this, a training set is prepared that finds the association between the values of predictors and the target. The target is the value assigned to the class and the predictor is the value associated with the do- main whose target class needs to be found. The major classification techniques employed are Naïve Bayes Algorithm, Decision Tree and Support Vector Machines (SVM). This technique finds the significant application in the detection of credit card fraud, and suspicious emails. 2. Clustering: Cluster analysis, or clustering, is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to a set of parameters than to those in other groups. These groups are known as clus- ters. Cluster analysis is one of the main tasks in the field of data mining and is a commonly used xvi
  • 22. Preface technique for the statistical analysis of data. Cluster analysis does not refer to an algorithm but an exercise that has to be undertaken on the given data set. Various algorithms can be used for cluster analysis. The algorithms are divided into various categories and they differ significantly in their idea of what a cluster is constituted of and how the clusters are identified. The most popular ideas on the basis of which clusters are defined and identified include groups with small distances among the constituent members, areas of the data space which are highly dense, intervals or particular distributions. Clustering is a multi-objective problem that it is a mathematical optimization prob- lem. A clustering algorithm consists of parameter settings such as a distance function, a density threshold (the number of clusters expected to be formed). Based on the available data set and the use of result as intended by the user, apt clustering algorithm may be used. 3. Association Rule Mining: In association rule mining, the association between item sets are con- sidered or found with the help of Support and Confidence. The Rule are framed according to the data values and corresponding relationship between them. 4. Neural Network: A Neural Network (NN) is used to recognize patterns in data. The data can be specified according to the different domains like Financial Fraud including Credit Card Fraud de- tection and phishing, etc. NNs are used for those problems where the exact solution is not required, such that this technique is not sensitive to errors. Some common types of NNs are Artificial Neural Networks (ANNs) and Multilayer Artificial Neural Networks (MNNs). 5. Genetic Algorithms: Genetic Algorithms (GAs) predict using generated logic rules and fitness functions in order to detect financial fraud and suspicious e-mails. The major steps used are Mutation, Inheritance, Selection and Crossover. GAs and NNs can be used in combination to solve a complex problem. Every model inherits traits from previous models and compares it with the other models to more accurately model remains. It is based on the theory of the survival of the fittest, which means that the model which is fit will survive to the next generation and the others will not be applied to the next level. These techniques are able to classify the given data on the basis of whether it is supervised or unsu- pervised learning methodologies. In case of supervised learning, the dependent and independent vari- ables are considered. There are a set of independent variables based on which the value of the dependent variable is predicted, while in the case of unsupervised learning, the useful information is searched by forming clusters or groups. The variables in both cases can be nominal, ordinal, categorical or continu- ous variables depending upon the available data which enables us to apply the various algorithms of the different techniques discussed above. Collaborative filtering finds its roots in data-mining. Data mining is finding hidden and unknown information from large databases. The data mining tools and techniques are finding its immense applications in the modern day. Such an application is being proposed by the editor of this book which aims to find the data mining applications in emerging areas. These areas are already hot topics in the research. By including data mining in such areas, the application and usability of all said areas will be widened. The researchers are already working in the area of Collaborative filter- ing using the traditional methodologies. The editors are finding the data mining applications in this field with a motive of developing an effective recommendation system with accurate and precise information at the disposal of the users. Collaborative filtering is defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets, such as viewpoints, multiple agents and pre-existing data xvii
  • 23. Preface about the users’ behavior stored in matrices. Collaborative filtering is required when a huge data set is present. The collaborative filtering methods are used to create recommender systems for a wide variety of fields with lots of data having varied formats, such as sensing and monitoring of data in battlefields, line of controls and mineral exploration; financial data of institutions that provide financial services, such as banks and stock markets; sensing of large geographical areas from which data is received from all kinds of sensors and activities; ecommerce and websites where the focus is to recommend products to users to increase sales, to name a few. A definition of collaborative filtering, which is somewhat newer and a bit narrow in sense states that it is a way of automating the process of making predictions, a process which is known as filtering, about the preferences and dislikes of a user by collecting data from as big a number of users as possible, a process which is known as collaborating, hence the name collaborative filtering. The underlying as- sumption of the collaborative filtering approach is that if a person A has the same opinion of an issue as a person B, then A is more likely to have an opinion similar to B’s opinion on a related but different issue. It is noteworthy that such predictions are specific to the user, but they are formed by using data from a number of users. The personal information of the user such as age, gender and location are generally not used in collaborative filtering (CF) but a partially observed matrix of ratings is used. The rating matrix may be binary or ordinal. The binary matrix contains the ratings by the users in columns in the form of likes or dislikes while the user’ name or id is in the rows. The ordinal matrix contains ratings in form of a number of responses from the user such as excellent, very good, good, average, poor or simply in form of stars out of five or ten, a system that is used frequently in this day and age. The rating matrix can easily be gathered implicitly by the website’s server, for example using click stream logging. Clicks on links to pages of goods or services provided can be considered to be a positive review of the user. While the rating matrices can prove to be useful, one major drawback is that they are extremely sparse, so it is very difficult to clump similar users together into classes. This is due to each and every user does not give reviews about each and every product. Thus, collaborative filtering consists of storing this sparse data and analyzing it to create a recommendation system. The objective of the proposed publication was to make aware researchers and other prospective read- ers with latest trends and patterns in the inclusion of the data mining tools and techniques in the areas of Collaborative filtering which helps to develop a system with precise knowledge and accuracy for helping the users of the system. The inclusion of improved and proven algorithms of the data mining helps to extract the nuggets of hidden and unknown information which helps to frame an effective recommenda- tion system using Collaborative filtering. The mission of the proposed publication was to come up with an edited book which aims at being the latest and most advanced topic inclusion and simultaneously acts as a discussion of the contributions of renowned researchers whose work has created a revolution in this area. The contributions by eminent researchers in fields of data mining, opinion mining, senti- ment analysis and Collaborative filtering will be part of book in emerging e-areas like retail, financial institutions and social networks. The objective would be to cover each and every aspect of Collaborative filtering, such as memory-based, model-based and Hybrid methodologies. The unique characteristics of the publication were: 1. The proposed work of eminent researchers in the aspect of Collaborative filtering—like memory- based, model-based and Hybrid methodologies—in areas such as retail, financial institutions and social networks which are current focuses of research will be part of the proposed publication. xviii
  • 24. Preface 2. The proposed publication will be targeted towards providing the highest quality, most accurate and latest research by eminent researchers considering the facts of how such research affects and influ- ences common people in their everyday lives with effective and precise recommendation systems. 3. The area which will be part of published work will have a significant influence on business users, common people and have a great impact on society. In August 2015, in the call for chapters, I urged and sought contributions to this book from researchers, IT savvy’s, and young Engineers across the globe with an aim to extract and accumulate the modern day research in the field of Collaborative Filtering Using Data Mining and Analysis, and gradually I started receiving quality and very conceptual, basic and advanced contributions from different contributors from across the globe. Initially, I thought as whether I will be getting any chapters on this topic as it is very new and emerging area, but surprisingly I saw a great response with authors started to respond, which encouraged me and motivated me by showing that this area is gaining importance. After screening through them, my objective was clear, this aimed and concentrated on getting chapters which focused on elementary issues, needs, and the demand for Collaborative Filtering. The book is a collection of the fourteen chapters which have been written by eminent professors, researchers, and industry people from different countries. These chapters were initially peer-reviewed by the Editorial board members, reviewers, and industry people who themselves span over many countries. The book is divided into three sections: Section 1, Data Mining techniques and analysis: An Overview; Section2,Collaborativefiltering:AnIntroduction;andSection3,Applicationsofdataminingtechniques and data analysis in collaborative filtering. SECTION 1: DATA MINING TECHNIQUES AND ANALYSIS: AN OVERVIEW Chapter 1 by Dr. Renuka Mahajan, revolves around the synthesis of three research areas- data min- ing, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what’ can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what’ can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning. Chapter 2 by Mamta Mittal, Dr. R.K. Sharma, Dr. V.P. Singh and Lalit Mohan Goyal enlightened that Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired xix
  • 25. Preface distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five ex- periments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets. In Chapter 3 by Neethu Akkarapatty, Anjaly Muralidharan, Nisha S. Raj and Dr. Vinod P underlined that Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotionsfromsentences.Sentimentanalysisistheprocessusedtodeterminetheattitude/opinion/emotions expressed by a person about a specific topic based on Natural Language Processing (NLP). Proliferation of social media such as blogs, Twitter, Facebook and LinkedIn has fuelled interest in Sentiment analy- sis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassifications is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains. SECTION 2: COLLABORATIVE FILTERING: AN INTRODUCTION Chapter 4 by Venkatesan M and Dr. Thangadurai K analyzes the recommender systems, their history and its framework in brief. The current generation of filtering techniques in recommendation methods can be broadly classified into the following five categories. Techniques used in these categories are discussed in detail. Data mining algorithms techniques are implemented in recommender systems to filters user data ratings. Area of application of Recommender Systems gives broad idea and such as how it gives impact and why it is used in the e-commerce, Online Social Networks (OSN), and so on. It has shifted the core of Internet applications from devices to users. In this chapter, issues and recent research in recommender system are also discussed. In Chapter 5 by Neeti Sangwan and Naveen Dahiya urged that Recommendation making is an impor- tant part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommenda- tions to the users, efficient and cost effective methods needs to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests. Chapter 6 by Amrit Pal and Dr. Manish Kumar describes that Size of data is increasing; it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collabora- tive filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, map reduce based collaborative filtering are some of these techniques. Chapter addresses these techniques and some basics of collab- orative filtering In Chapter 7 by Anu Saini focused that today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behaviour. Recommendation systems are used to provide the recom- xx
  • 26. Preface mendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering. SECTION 3: APPLICATIONS OF DATA MINING TECHNIQUES AND DATA ANALYSIS IN COLLABORATIVE FILTERING Arushi Jain, Dr. Vishal Bhatnagar and Pulkit Sharma in Chapter 8 canvass that there is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to a certain set of parameters than to those in other groups. These groups are known as clusters. Cluster analysis is one of the main tasks in the field of data mining and is a commonly used technique for statistical analysis of data. While big data collaborative filtering defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets such as viewpoints, multiple agents and pre-existing data about the users’ behaviour stored in matrices. Collaborative filtering is especially required when a huge data set is present. In chapter 9 Prof. Carson K. Leung, Fan Jiang, Edson M. Dela Cruz and Vijay Sekar Elango presents that Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valu- able to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering. Chapter 10 by Mahima Goyal and Dr. Vishal Bhatnagar discusses that the recent trend of express- ing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature review about the different tech- niques and applications of this field. The primary techniques can be classified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering. xxi
  • 27. Preface Sheng-Jhe Ke and Wei-Po Lee in Chapter 11 emphasise that Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbours and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbours. In this chapter, we present a data analytics approach that combines user preference and social trust. This approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can enhance the recommendation performance in an adaptive way without an iterative parameter-tuning process. In Chapter 12 Dr. Marenglen Biba, Dr. Narasimha Rao Rao Vajjhala and Lediona Nishani provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter will begin with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collabora- tive filtering approaches will be presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. The chapter will also include the key section of the discussion on the latest trends in visual data mining for collaborative mining. Chapter 13 by Snehalata Sewakdas Dongre and Dr. Latesh Malik explored that A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly. Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play im- portant role here how the different classifiers work collaborative within the ensemble to achieve a goal. Lediona Nishani and Prof. Marenglen Biba in Chapter 14 presents that people nowadays base their behaviourbymakingchoicesthroughwordofmouth,media,publicopinion,surveys,etc.Oneofthemost prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filter- ing area and suggest further open issues in order to advance in this field of research. The applications of Collaborative Filtering Using Data Mining and Analysis are so vast that it cannot be covered in single book. However with the encouraging research contribution by the researchers in xxii
  • 28. Preface this book, we (contributors) tried to sum the latest development and work in the area. This edited book will serve as the stepping stone and a factor of motivation for those young Researchers and Budding Engineers who are witnessing the every stopping growth in the field of Collaborative Filtering Using Data Mining and Analysis. Vishal Bhatnagar Ambedkar Institute of Advanced Communication Technologies and Research, India xxiii
  • 29.  Acknowledgment  No work big or small is an accomplishment of any individual but a consistent and coordinated effort of a clique. Another driving force is the encouragement and guidance provided by friends and family, their words of motivation prove to be the guiding light in times of distress. I would like to express my heartfelt gratitude to all those, whose unremitting efforts helped me realize this piece of literature. First of all, I would like to thank my colleagues at the publishing team at Idea Group Publishing for their wonderful collaboration and timely reminder for the needful action at my end. You have sup- ported me incredibly throughout. In particular, I would like to single out the contributions of Courtney Tychinski whose continuous suggestions and valuable information kept me motivated also served as a timely reminder for the completion of the work. A journey of thousand miles begins with a single step, I would like to Kayla Wolfe for helping me with the inception of this book, I would also like to extend my gratitude to Jan Travers for timely completion of the contract agreement and helping me to finally decide the title of the book which I feel is essential for attracting the prospective contributors. I would like to thank all other support staff of Idea Group for extending their full support. A special word of mention to Professors and researcher’s across globe for agreeing to be part of the EAB and helping me to find a professor to write the Foreword. Thanks a ton Sir! Secondly, the editor would like to thank each one of the authors for their contributions. My sincere gratitude goes to the chapter’s authors who contributed their time and expertise to this book. I wish to thank the authors for their meticulous efforts and unparalleled perseverance that paved the way for the success of the project. I am also deeply indebted to all the reviewers, there deep insights regarding the improvement of quality, coherence, and content presentation of chapters were invaluable. Most of the authors also served as referees; I highly appreciate their double task their suggestions have contributed immensely to change the structure of the chapters and have transformed the book into its current form. Anyprojectcannotsucceedwithoutaconduciveenvironment.Iwouldliketotakethisopportunitytothank my colleagues at AIACTR for being the fulcrum of guidance motivation. I would like to thank Prof Ashok Mittal for his words of encouragement and invigoration. Also, I would like to thank my students Amit Kumar, Pulkit Sharma and Komal Mahajan for their help during the whole development process. At last, I would like to thank my parents for their blessing and words of wisdom, the values of hard work, passion and perseverance they inculcated in me as a child, have helped me sail through all the challenges I faced during the completion of this book. No words are enough to thank my beloved wife for standing xxiv
  • 30. Another Random Document on Scribd Without Any Related Topics
  • 31. Mr. Rankin. Had your husband said anything before or did he say anything at that time in regard to Mr. Nixon showing any hostility, friendship, or anything else? Mrs. Oswald. Showing any hostility or friendship toward Mr. Nixon? Mr. Rankin. Yes; toward Nixon. Mrs. Oswald. I don't remember him saying anything—I don't remember but he didn't tell me. I don't remember him saying anything of that sort. I only remember the next day he told me that Nixon did not come. Excuse me. Mr. Rankin. Yes. Mrs. Oswald. The FBI suggested that possibly I was confused between Johnson and Nixon but there is no question that in this incident it was a question of Mr. Nixon. I remember distinctly the name Nixon because I read from the presidential elections that there was a choice between President Kennedy and Mr. Nixon. Representative Ford. Where did your husband get the pistol that morning; do you remember? Mrs. Oswald. What, where? Representative Ford. Where. Mrs. Oswald. My husband had a small room where he kept all that sort of thing. It is a little larger than a closet. Representative Ford. Did you see him go in and get the pistol? Mrs. Oswald. I didn't see him go into the room. I only saw him standing before the open door and putting the pistol in his pocket. Representative Ford. Do you recall which pocket he put the pistol in? Mrs. Oswald. It was not in a pocket. He put it in his belt. (Discussion off the record.)
  • 32. Mr. Dulles. Had you and your husband ever discussed Mr. Nixon at a previous, at any previous time? Mrs. Oswald. No. No. Mr. Rankin. What else happened about this incident beyond what you have told us? Mrs. Oswald. He took off his suit and stayed home all day reading a book. He gave me the pistol and I hid it under the mattress. Mr. Rankin. Did you say anything more than you have told us to him about this matter at that time? Mrs. Oswald. I closed the front door to the building that day and when we were quarreling about—when we were struggling over the question of whether or not he should go out I said a great deal to him. Mr. Rankin. What did you say to him then? Mrs. Oswald. I don't remember. Mr. Rankin. Just tell us in substance? Mrs. Oswald. I really don't remember now. I only remember that I told him that I am sorry of all these pranks of his and especially after the one with General Walker, and he had promised me, I told him that he had promised me—— Mr. Rankin. Did he say anything in answer to that? Mrs. Oswald. I don't remember. Mr. Dulles. As I recall, in your previous testimony there was some indication that you had said that if he did the Walker type of thing again you would notify the authorities. Did that conversation come up at this time with your husband? Mrs. Oswald. Yes; I said that. But he didn't go at that time and after all he was my husband.
  • 33. Mr. Dulles. Does—do you mean you said it again at the time of the Nixon incident? Mrs. Oswald. Yes; I told him that but you must understand that I don't speak English very well, and for that reason I used to keep a piece of paper with me, and I had it, you know, what piece of paper I am talking about. At that time I didn't know how to go in police station: I don't know where it was. Mr. McKenzie. Was that the passport? Mrs. Oswald. No. After the incident with Walker—— Mr. Rankin. Was that paper the Walker incident note that you have described in your testimony? Mrs. Oswald. Yes. Representative Ford. When you put the pistol under the mattress, what happened to the pistol from then on? Mrs. Oswald. That evening he asked for it and said that nothing was going to happen, and that he said he wouldn't do anything and took the pistol back. And put it into his room. Mr. Dulles. Did you keep the, what you call, the Walker note with you all the time or did you have it in a particular place where you could go and get it and show it to him? Mrs. Oswald. I had it all the time. I kept it in a certain place initially and then I put it in the pages of a book. Senator Cooper. Mr. Rankin, would you ask the witness to state again what Lee Oswald's promise was to her that he had made at the time of the Walker incident? Mr. Rankin. Will you relate the promise that your husband made to you right after the discovery of the Walker incident by you? Mrs. Oswald. This wasn't a written promise. Mr. Rankin. No.
  • 34. Mrs. Oswald. But in words it was more or less that I told him that he was very lucky that he hadn't killed—it very good that he hadn't killed General Walker. I said it was fate that—it was fated that General Walker not be killed and therefore he shouldn't try such a thing again. Mr. Rankin. What did he say in answer to that? Mrs. Oswald. He said perhaps I am right. I myself didn't believe what I was saying because I didn't believe that he was fated. I was just trying to find some way of dissuading my husband to do such a thing again. Do you understand what I mean? Mr. Rankin. Yes. Did he say that he would or would not do that again, that is what I want to know. Mrs. Oswald. At the time I did definitely convince him that I was right, and at the time he said that he would not do such a thing again. Mr. Rankin. Now, when you talked to him about the Nixon incident and persuaded him not to go out and do anything to Mr. Nixon, did you say anything about your pregnancy in trying to persuade him? Mrs. Oswald. Yes. Mr. Rankin. What did you say about that? Mrs. Oswald. Yes; I told him that I was pregnant. Mr. Rankin. Did you observe his action at the time of this Nixon incident, how he acted? Mrs. Oswald. How he reacted to this? Mr. Rankin. How he reacted to your interfering with him. Mrs. Oswald. At first he was extremely angry, and he said, You are always getting in my way. But then rather quickly he gave in, which was rather unusual for him. At the time I didn't give this any thought, but now I think it was just rather a kind of nasty joke he
  • 35. was playing with me. Sometimes Lee was—he had a sadistic—my husband had a sadistic streak in him and he got pleasure out of harming people, and out of harming me, not physically but emotionally and mentally. Mr. Rankin. Have you told us substantially all that happened about this Nixon incident? Mrs. Oswald. That is all I can remember. Representative Ford. Can you tell us why you didn't mention this incident to the Commission when you appeared before? Mrs. Oswald. There were an awful lot of questions at that time, and I was very tired and felt that I had told everything and I don't remember, I can't understand why I didn't mention this. It would have been better for me to mention it the first time than to make you all do more work on it. Mr. Dulles. At the time of this incident did you threaten to go to the authorities in case your husband did not desist in his intention? Mrs. Oswald. Yes; I said that. Senator Cooper. I may have to go—could I ask a few questions? Mrs. Oswald, will you repeat what your husband said that morning when he dressed and got the pistol? Mrs. Oswald. I asked him where he was going and why he was getting dressed. He answered. Today Nixon is coming and I want to go out and have a look at him. I answered, I know how you look, and I had in mind the fact that he was taking a pistol with him. Senator Cooper. Did he say anything about what he intended to do with the pistol? Mrs. Oswald. No. Senator Cooper. Did you ask him if he intended to use the pistol against Mr. Nixon?
  • 36. Mrs. Oswald. I told him that, You have already promised me not to play any more with that thing. Not really play, but, you know—I didn't mean, of course, just playing but using the pistol. Then he said, I am going to go out and find out if there will be an appropriate opportunity and if there is I will use the pistol. I just remembered this and maybe I didn't say this in my first testimony and now it just has occurred to me that he said this. Senator Cooper. Did your husband say why he wanted to use the pistol against Mr. Nixon? Mrs. Oswald. No. Senator Cooper. Did he say where he intended to see Mr. Nixon? Mrs. Oswald. He didn't say. He just said in Dallas, and since Nixon was coming to Dallas. Senator Cooper. When he was talking to you about seeing Mr. Nixon and using the pistol, what was his attitude? Was he angry or—— Mrs. Oswald. He wasn't angry. He looked more preoccupied and had sort of a concentrated look. Senator Cooper. Now, from the beginning, from the time that he first told you that he was going to use the pistol, until the time that you say he became quieted, did he again make any statement about using the pistol against Mr. Nixon? Mrs. Oswald. I told him that I didn't want him to use his gun any more. He said, I will go out and have a look and perhaps I won't use my gun, but if there is a convenient opportunity perhaps I will. Strike perhaps please from that last sentence. I didn't have a lot of time to think of what we were actually saying. All I was trying to do was to prevent him from going out. Senator Cooper. How much time elapsed, if you can remember, from the time he first told you that he was going out and when he finally became pacified?
  • 37. Mrs. Oswald. This was maybe 30 minutes. The whole incident took maybe 20 minutes. It was about 10 minutes I took—15 minutes maybe. 15 minutes, it took maybe 10 minutes for him to be prepared to go out and then the incident in the bathroom took maybe 5 minutes until he quieted down. It doesn't mean I held him in the bathroom for 5 minutes because I couldn't do that but the general discussion in the bathroom. Senator Cooper. You said he stayed at the house the remainder of the day. During the remainder of the day did you discuss again with him the incident? Mrs. Oswald. No; no. Senator Cooper. Did he say anything more that day? Mrs. Oswald. No. He read a book. Mr. Dulles. Do you know what book it was, by chance? Mrs. Oswald. I don't remember. It was some kind of book from the public library. He had a two-volume history of the United States. This is not from the library, this was his own book. Mr. Dulles. The incident occurred, you said just a few days after he had told you he shot at General Walker? Mrs. Oswald. It was about 10 or 12 days after the incident with General Walker, perhaps about 3 days before we left for the departure for New Orleans. This didn't happen right after the incident with General Walker. It happened rather closer to a time when we departed for New Orleans. Mr. Dulles. The General Walker incident made a very strong impression on you, didn't it? Mrs. Oswald. Of course. I never thought that Lee had a gun in order to use it to shoot at somebody with. Mr. Dulles. Didn't this statement that he made about Vice President Nixon make a strong impression on you also?
  • 38. Mrs. Oswald. I don't know. I was pregnant at the time. I had a lot of other things to worry about. I was getting pretty well tired of all of these escapades of his. Mr. Dulles. Was there any reason why you didn't tell the Commission about this when you testified before? Mrs. Oswald. I had no—there is no particular reason. I just forgot. Very likely this incident didn't make a very great impression on me at that time. Mr. Dulles. Now, before the death of President Kennedy, of course, you knew that your husband had purchased a rifle? Mrs. Oswald. Yes. Mr. Dulles. You knew that he had purchased a pistol? Mrs. Oswald. Yes. Mr. Dulles. And a knife? Mrs. Oswald. No; what kind of knife? Mr. Dulles. Did he have a knife? Mrs. Oswald. He had a little pocket knife; I think. Mr. Dulles. You knew that he had told you that he had tried to kill General Walker? Mrs. Oswald. Yes. Mr. Dulles. And, of course, as you said you heard him make a threat against Nixon. Mrs. Oswald. Yes. Mr. Dulles. Did you have some fear that he would use these weapons against someone else? Mrs. Oswald. Of course; I was afraid. Mr. Dulles. What? Mrs. Oswald. Of course; I was afraid.
  • 39. Mr. Dulles. You thought that he might use his weapons against someone? Mrs. Oswald. After the incident with Nixon I stopped believing him. Mr. Dulles. You what? Mrs. Oswald. I stopped believing him. Mr. Dulles. Why? Mrs. Oswald. Because he wasn't obeying me any longer, because he promised and then he broke his promise. Mr. Dulles. Would you repeat that? Mrs. Oswald. Because he wasn't obeying me any more. He promised and, he made a promise and then he broke it. Mr. Dulles. That is my question. Having been told that—isn't it correct he told you that he shot at General Walker? He made a promise to you that he wouldn't do anything like that again, you heard him threaten Vice President Nixon, didn't it occur to you then that there was danger that he would use these weapons against someone else in the future? Mrs. Oswald. After the incident with Walker, I believed him when he told me that he wouldn't use the weapons any longer. Mr. Dulles. I remember you testified before and I asked you if you had heard him threaten any official or other person and your answer was no. Mrs. Oswald. Because I forgot at that time about the incident with Nixon. Mr. Dulles. I want to ask you again: In view of the fact that you knew—in view of the fact that he had threatened Walker by shooting at him, and he threatened Vice President Nixon can you not tell this Commission whether after that he threatened to hurt, harm any other person?
  • 40. Mrs. Oswald. Nobody else. Perhaps I should be punished for not having said anything about all this, but I was just a wife and I was trying to keep the family together, at that time. I mean to say. I am talking, of course, of the time before President Kennedy's death. And if I forget to say anything now, I am not doing it on purpose. Mr. Dulles. I am just asking questions. Will you say here that he never did make any statement against President Kennedy? Mrs. Oswald. Never. Mr. Dulles. Did he ever make any statement about him of any kind? Mrs. Oswald. He used to read and translate articles from the newspaper about Kennedy to me and from magazines, favorable articles about Kennedy. He never commented on them and he never discussed them in any way but because of his translations and his reading to me he always had a favorable feeling about President Kennedy because he always read these favorably inclined articles to me. He never said that these articles never were true that he was a bad President or anything like that. Mr. Dulles. I didn't catch the last. Mrs. Oswald. He never said these articles were not true or that President Kennedy was a bad President or anything like that. Senator Cooper. I think you testified before that he made statements showing his dislike of our system of government and its economic system. Mrs. Oswald. He used to complain about the educational difficulties and about the unemployment in the United States and about the high cost of medical care. Mr. McKenzie. Right there, please, may I, Mr. Dulles when did he complain of those things, was this in Russia or was it in the United States after you returned from Russia?
  • 41. Mrs. Oswald. After our return from Russia. When we were living in New Orleans after returning from Russia. Mr. McKenzie. Did he likewise make such complaints about the American system while you were living in Russia after you were married? Mrs. Oswald. He used to tell me that it was difficult to find a job and to get work in the United States but nonetheless we would be better there than we were in Russia. Excuse me. He was the kind of person who was never able to get along anywhere he was and when he was in Russia he used to say good things about the United States and when he was in the United States he used to talk well about Russia. Senator Cooper. You knew, of course, because of the incidents in New Orleans that he did not like American policy respecting Cuba. Mrs. Oswald. He was definitely a supporter of Cuba. This was something which remained with him from Russia. Senator Cooper. Did he ever say to you who was responsible or who had some responsibility for our policy toward Cuba? Mrs. Oswald. No. Senator Cooper. Had he ever mentioned President Kennedy in connection with our Cuban policy? Mrs. Oswald. Never to me. Mr. Dulles. Did he ever say anything—— Mrs. Oswald. He might have discussed this with Paine. Senator Cooper. With who? Mrs. Oswald. Mr. Paine, husband of Ruth Paine. Senator Cooper. He might have done what now? Mrs. Oswald. With the husband of Ruth Paine.
  • 42. Senator Cooper. Why do you say that, did you ever hear him talking about it? Mrs. Oswald. He used to talk politics with Mr. Paine. I don't know what they were talking about because at that time I didn't understand English. Senator Cooper. Did you mean, though, to say that you believed he might have discussed the Cuban policy with Mr. Paine. Mrs. Oswald. Yes; especially after we returned from New Orleans. Senator Cooper. Why? Why do you make that statement? Mrs. Oswald. Because we only saw Mr. Paine once or twice before we went to New Orleans. And there was more opportunity to see Mr. Paine after we came back. Senator Cooper. But my question is what makes you think he might have talked to Mr. Paine about Cuba? Mrs. Oswald. I think, sir; because after returning from New Orleans this was his favorite subject, Cuba, and he was quite—a little bit cracked about it, crazy about Cuba. Senator Cooper. You mean he talked to you a great deal about it after you came from New Orleans? Mrs. Oswald. Well, in New Orleans he used to talk to me endlessly about Cuba, but after we came back he didn't talk to me about it any longer because I was just sick and tired of this. Mr. Dulles. He in this case is your husband? Mrs. Oswald. That is right. I really don't know about what he talked with Mr. Paine. I think that they were talking about politics, that is to say my husband with Mr. Paine because my husband used to tell me afterwards, Well, he doesn't understand anything about politics. He is not too strong on politics. And, therefore, I think they were probably talking with the American political system and the Russian political system and
  • 43. comparisons between them. I think that Mr. Paine could probably tell you more about this than I can. Senator Cooper. That is all I want to ask for the time being. Mrs. Oswald. I think that Mr. Paine knows more about my husband's political attitudes toward the United States than I do. Mr. Rankin. You said the FBI asked you whether you could have been mistaken about it being Mr. Nixon that your husband was interested in going and seeing and maybe doing something to with his gun. Do you know what Mr. Johnson you were asking about? Let me rephrase the question. You said the FBI asked you whether you might have been mistaken about Mr. Nixon and whether it might have been Mr. Johnson instead of Mr. Nixon that your husband was interested in doing something to with his gun. Do you know what Mr. Johnson was being referred to? Mrs. Oswald. No; I didn't know who Johnson was. I am ashamed but I never knew his name. I am ashamed myself but I didn't know who Johnson was. Mr. Rankin. You didn't know that the FBI was asking about the then Vice President and now President Johnson? Mrs. Oswald. No; I never heard of Johnson before he became President. Mr. Dulles. And you are quite sure—— Mrs. Oswald. Maybe I am stupid, I don't know. Mr. Dulles. And you are quite sure that your husband mentioned the name of Nixon to you—— Mrs. Oswald. Yes; I am sure it was Nixon. Mr. Dulles. That morning?
  • 44. Mr. Rankin. Do you know whether this Nixon incident occurred the day before your husband went to New Orleans? Mrs. Oswald. It wasn't the day before. Perhaps 3 days before. Mr. McKenzie. Mr. Rankin, may I ask a question? Mr. Rankin. Yes. Mr. McKenzie. Mrs. Oswald, you say or you said a few minutes ago that Mr. Paine knew or knows more about your husband's attitude about the United States than you do. Why did you say that? Mrs. Oswald. Because my husband's favorite topic of discussion was politics, and whoever he was with he talked to them politics and Mr. Paine was with him a fair amount and I am not sure they talked about politics. They went to meetings of some kind together, I don't know what kind of meetings. Mr. McKenzie. Do you know where the meetings were? Mrs. Oswald. In Dallas. After they came back from some meeting my husband said to me something about Walker being at this meeting, and he said, Paine knows that I shot him. I don't know whether this was the truth or not. I don't know whether it was true or not but this is what he told me. Mr. McKenzie. Would they go in Mr. Paine's automobile? Mrs. Oswald. Yes; it was about 2 days after this incident with Stevenson or the next day, or maybe it was the same place, or the next day that a meeting was held where General Walker appeared. Mr. McKenzie. It was the day before. Mrs. Oswald. The day before? The day after. I think there was 1 day's difference between them, either it was the day before or the day after. Mr. Rankin. Did you say that there were a number of political meetings——
  • 45. Mrs. Oswald. Excuse me; but I think this was on Friday. I think that Lee was at this meeting on a Friday. Mr. Rankin. Did you say there were a number of political meetings that your husband went to—— Mrs. Oswald. Excuse me; this was October 24. Mr. Rankin. With Mr. Paine? Mrs. Oswald. A week after his birthday—this was Friday. I think it was a week after my husband's birthday about October 24 or something like that or the 25th. Mr. Rankin. Mr. Reporter, can you give her the question that I asked? Mrs. Oswald. Excuse me, please. (The question was read by the reporter.) Mrs. Oswald. I only know about this one. Mr. Rankin. Did the FBI tell you that the reason they were asking about whether there was a mistake as to whether it was Mr. Nixon or Vice President Johnson was because there was a report in Dallas papers about Vice President Johnson going to Dallas around the 23d of April? Mrs. Oswald. Yes; they did tell me this. They said that at this time there was only one announcement in the newspapers of anyone coming and that was Vice President Johnson. Mr. Rankin. But you still are certain it was Mr. Nixon and not Vice President Johnson? Mrs. Oswald. Yes, no. I am getting a little confused with so many questions. I was absolutely convinced it was Nixon and now after all these questions I wonder if I am right in my mind. Mr. Rankin. Did your husband—— Mrs. Oswald. I never heard about Johnson. I never heard about Johnson. I never knew anything about Johnson. I just don't think it
  • 46. was Johnson. I didn't know his name. Mr. Rankin. Did you husband during the Nixon incident say Mr. Nixon's name several times or how many times. Mrs. Oswald. Only once. Mr. Rankin. Now, you said that your husband went to get the pistol in the room. Will you tell us what room that was that he went to get the pistol? Mrs. Oswald. It was a small sort of storeroom. Just to the left off the balcony as you come in; it is just on the left from the balcony. Mr. Rankin. Was it out, was the pistol out in the room or was it in a closet? Mrs. Oswald. This room contained only a table and some shelves, and the pistol was not on the table. It was hidden somewhere on a shelf. Representative Ford. Was the rifle in that room, too? Mrs. Oswald. Yes. Mr. Rankin. Where was the rifle in the room? Mrs. Oswald. Sometimes it was in the corner, sometimes it was up on a shelf. Lee didn't like me to go into this room. That is why he kept it closed all the time and told me not to go into it. Sometimes he went in there and sat by himself for long periods of time. Mr. Dulles. By closed, do you mean locked? Mrs. Oswald. He used to close it from the inside. I don't remember what kind of lock it was. Possibly it was just a—some kind of a tongue—— Mr. McKenzie. Latch. Mrs. Oswald. Latch or something like that. Mr. Dulles. How could he close it from the inside and then get out?
  • 47. Mrs. Oswald. When he was inside he could close it from the inside so that I couldn't come in. Mr. Dulles. But when he came out could he close it from the outside so that you could not get in? Mrs. Oswald. No; from the outside it couldn't be locked. Representative Ford. When you went to New Orleans and packed for the trip to New Orleans, did you help to pack the pistol or the rifle? Mrs. Oswald. No, no; Lee never let me pack things when we went for trips. He always did it himself. Representative Ford. Did you see him pack the pistol or the rifle? Mrs. Oswald. No. Representative Ford. Did you know the pistol and the rifle were in the luggage going to New Orleans? Mrs. Oswald. I stayed for some time with Ruth Paine after he left for New Orleans and I don't know whether they were in his things or they were in the stuff which was left with me. Representative Ford. At the time Mrs. Paine picked you up to go to the bus station, did you intend to go by bus to New Orleans at that time? Mrs. Oswald. No. Representative Ford. While you were living on Neely Street you didn't tell us before of any extensive rifle shooting at Love Field or rifle practice at Love Field. Can you tell us more about it now? Mrs. Oswald. Lee didn't tell me when he was going out to practice. I only remember one time distinctly that he went out because he took the bus. I don't know if he went to Love Field at that time. I don't—after all this testimony, after all this testimony, when I was asked did he clean his gun a lot, and I answered yes, I
  • 48. came to the conclusion that he was practicing with his gun because he was cleaning it afterwards. Representative Ford. Did he take the rifle and the pistol to Love Field or at the time he went on the bus? Mrs. Oswald. Only the rifle. Mr. McKenzie. Just a minute. Let me ask her a question. May I ask a question? Representative Ford. Yes, sir. Mr. McKenzie. Representative Ford, I wasn't here as you know when Mrs. Oswald testified before. I have been with her when she was interrogated by the FBI relative to practicing the rifle shooting. This is the first time that I have heard the use of the words Love Field. Has there been prior testimony by Mrs. Oswald here that he was practicing at Love Field, because the reason I ask this is because she has steadfastly in the past told me and the FBI that she didn't know where he went to practice and that is the reason I wanted to know. Mr. Rankin. The record is—— Mrs. Oswald. I don't know where he practiced. I just think that the bus goes to, went to Love Field. Mr. Rankin. Her testimony before was that the bus that he took, that she knows about when he went, was a bus that went to Love Field, and she thought he went to some place in that area to do his practicing. Mr. McKenzie. The reason I ask the question, Mr. Rankin, is because I don't believe there is any practice area at Love Field for rifle practicing. Mr. Rankin. Well, the investigation that the Commission has made shows that there is a place near Love Field where people do shooting and practicing. Mr. McKenzie. Not at Love Field.
  • 49. Mr. Rankin. It is right adjacent, in the neighborhood. Mrs. Oswald. Once we went out with Kathy Ford with the children to watch airplanes landing and these airplanes made a tremendous noise and for that reason I thought that maybe my husband was practicing somewhere in that area because you couldn't hear the sound of shots. I don't know if there is any place near there where one can practice shooting, though. This idea just came to me a little while ago when we were out there, watching the airplanes because it was a couple of weeks ago that this happened. Just sort of a guess of mine. Mr. Dulles. How did he pack the gun or conceal the gun when he went out on the bus toward Love Field? Mrs. Oswald. Are you talking about the gun or the rifle? Mr. Dulles. I am talking about the rifle. Mrs. Oswald. He used to wrap it up in his overcoat, raincoat. Mr. Rankin. So that the record will be clear on this, Mr. McKenzie, the prior testimony did not purport to indicate that Mrs. Oswald thought he was practicing right on Love Field where the airplanes were landing or anything like that. It was that he took that bus and took the rifle and came back with the rifle and that the bus went to Love Field and the investigation has shown that there is at least one place in that immediate neighborhood where there is gun practice carried on. Mr. Dulles. Is there testimony, Mr. Rankin, as to more than one trip or should we get that from the witness? Mr. Rankin. She testified right now she only knew of this one although she knew of his cleaning his guns a number of times. She just testified to that. Do you want more than that? Mr. Dulles. I thought the record was a little fuzzy. Maybe you should clarify it. Mr. McKenzie. I think you should ask the question.
  • 50. Mr. Rankin. Will you tell us, Mrs. Oswald, how you thought your husband might have been practicing in the area near Love Field or how you concluded that he might have been practicing with the rifle in the area near Love Field. Mrs. Oswald. Only because that is the bus, only because that is where the bus goes. He never told me where. Mr. Rankin. And you don't know whether he was practicing at a place near Love Field or some place between where he got on the bus near your home and Love Field; is that right? Mrs. Oswald. No; I don't know, even now I don't know where it is. Senator Cooper. Can I just ask a question? Do you know how many times he took the rifle from your home? Mrs. Oswald. Well—— Mr. Dulles. You are speaking of Neely Street. Mrs. Oswald. I only saw—— Senator Cooper. When you were living on Neely Street—strike that. You have told about his taking the rifle from the house on Neely Street and then later cleaning the rifle. Do you know how many times that occurred? Mrs. Oswald. I saw him take the rifle only once when we were living on Neely Street but he cleaned the rifle perhaps three or four times, perhaps three times—three times. Senator Cooper. Did he ever tell you that he was practicing with a rifle? Mrs. Oswald. Only after I saw him take the gun that one time. Senator Cooper. Did you ask him if he had been practicing with the rifle? Mrs. Oswald. Yes, I asked him. Senator Cooper. What did he say?
  • 51. Mrs. Oswald. He said yes. Senator Cooper. Did he ever give any reason why he was practicing with the rifle to you? Mrs. Oswald. He didn't give me a reason. He just said that for a man it is an interesting thing to have a rifle. I considered this some kind of a sport for him. I didn't think he was planning to employ it. I didn't take it seriously. (At this point, Senator Cooper left the hearing room.) Mr. Rankin. At the time of the Nixon incident did you know who Mr. Nixon was? Mrs. Oswald. I didn't know what position he held. I thought he was Vice President. Mr. Rankin. Did you ever check to see whether Mr. Nixon was in fact in Dallas anytime around that date? Mrs. Oswald. No. Mr. Rankin. After the day of the Nixon incident did you ever discuss that incident again with your husband? Mrs. Oswald. No. Mr. Rankin. Did the Nixon incident have anything to do with your decision to go to New Orleans to live? Mrs. Oswald. After the incident with Walker it became clear to me that it would be a good idea to go away from Dallas and after the incident with Nixon insisted—I insisted on it. Mr. Rankin. After the Nixon incident did you ever discuss that Nixon incident again with your husband? Mrs. Oswald. No. I don't know why. Perhaps it didn't make a very strong impression on me and that is why I didn't mention it in my first testimony. Perhaps it is because the first incident with Walker made such a strong impression that what happened afterward was somewhat effaced by it. I was so much upset by this incident with
  • 52. General Walker that I only just wanted to get away from Dallas as fast as possible. Mr. Rankin. Did you discuss the Nixon incident with anyone other than your husband before the assassination of President Kennedy? Mrs. Oswald. No. Mr. Rankin. Did you ever consider telling the police about the Walker and Nixon incidents? Mrs. Oswald. I thought of this but then Lee was the only person who was supporting me in the United States, you see. I didn't have any friends, I didn't speak any English and I couldn't work and I didn't know what would happen if they locked him up and I didn't know what would happen to us. Of course, my reason told me that I should do it but because of circumstances I couldn't do it. Mr. Rankin. When did you first tell something about the Nixon incident? Mrs. Oswald. It was after the assassination; we were in Martin's house and I think Robert was there also. That is when I first mentioned that. I don't remember whether I told them both at the same time or told Martin first and Robert second or Robert first and Martin second. Mr. Rankin. Do you know about when that was with reference to the time you moved in with the Martins? Mrs. Oswald. I think it was in the first month. I don't remember which day it was, though. Mr. Rankin. Do you recall whether you first told Robert about it some time in January of this year? Mrs. Oswald. I think it was earlier than that, early in December. Perhaps in the beginning of January, but I think it was before New Year's. Mr. Rankin. If Robert has stated that it was on a Sunday, January 12 of this year, do you think he is in error then?
  • 53. Mrs. Oswald. I don't think that Robert would make a mistake. I might make a mistake myself but I don't think he would make a mistake because he doesn't have quite as many, because he has not been in contact with quite as many of these events and doesn't have quite as much to remember as I have. And in general, I have a bad memory for figures. Mr. Rankin. Did you discuss the Nixon incident at anytime with Mr. Thorne or Mr. Martin, your agent? Mrs. Oswald. I told Martin about it but I don't think I told Thorne about it, and if Thorne learned about it it must have been from Martin. Mr. Rankin. You just related how you told Mr. Martin about it and the occasion in your testimony a moment ago; is that right? Mrs. Oswald. I am certain that these were the circumstances in which I told Martin about this. Whether or not the—it's possible I was just talking with Martin and his wife about Lee and it just came into my mind and I don't remember whether Robert was there or not, or whether I told Robert later. Mr. Rankin. Did anyone at anytime advise you or tell you not to tell the Commission about this incident? Mrs. Oswald. Martin told me that it is not necessary to mention this. But when they were asking me here in the Commission whether I had anything to add to my testimony, I really forgot about it. When Martin and I were talking about it he said, Well, try not to think about these things too much. Mr. Rankin. Did he say anything about why it wasn't necessary to tell about this incident? Mrs. Oswald. I don't remember. I don't think he told me why. Maybe he told me and I just didn't understand because I didn't understand English very well. Mr. Rankin. When you were telling about the Nixon incident you referred to your husband's sadistic streak. Do you recall that?
  • 54. Mrs. Oswald. Yes. Mr. Rankin. Can you tell us a little more about that, how it showed? Mrs. Oswald. Anytime I did something which didn't please him he would make me sit down at a table and write letters to the Russian Embassy stating that I wanted to go back to Russia. He liked to tease me and torment me in this way. He knew that this—he just liked to torment me and upset me and hurt me, and he used to do this especially if I interfered in any of his political affairs, in any of his political discussions. He made me several times write such letters. Mr. Dulles. I have just one question: What did you or your husband do with these letters that you wrote? Did any of them get mailed or did they all get destroyed? Mrs. Oswald. He kept carbons of these letters but he sent the letters off himself. Mr. Dulles. To the Russian Embassy? Mrs. Oswald. Yes; he didn't give me any money to buy stamps. I never had any pocket money of my own. Mr. Rankin. But the letters to the Embassy you are referring to are actual letters and requested—requests—they weren't practice letters or anything of that kind to punish you, were they? Mrs. Oswald. Yes; they were real letters. I mean if my husband didn't want me to live with him any longer and wanted me to go back, I would go back, not because I wanted to go back but I didn't have any choice. Mr. Rankin. I misunderstood you then because I thought you were describing the fact that he made you write letters as a part of this sadistic streak that would never be sent but what he actually did was have you prepare the letters and then he proceeded to send them, is that your testimony?
  • 55. Mrs. Oswald. He did send them and he really wanted this. He knew that this hurt me. Mr. Rankin. Those are the letters to the Russian Embassy we have introduced in evidence in connection with your testimony; is that right? Mrs. Oswald. Yes; those are the letters. Representative Ford. Did he ever show you replies to those letters? Mrs. Oswald. At first—yes; there were. At first I didn't believe that he was sending off those letters. Representative Ford. But you did see the replies? Mrs. Oswald. I received answers from the embassy. Mr. Rankin. Now, I will turn to another subject, Mrs. Oswald. Mr. Dulles. Would you like to have a 5-minute recess? We will proceed. Mr. Rankin. Now, Mrs. Oswald, I would like to ask you about the Irving Gun Shop in Dallas. Mrs. Oswald. The what? I don't know anything about this at all. Mr. Rankin. Your counsel tells me I should correct that, that Irving is not a part of Dallas. It is the city of Irving. A witness has said that you and your two children and your husband came into a furniture shop asking the location of a gunshop in that area in Irving, and after appearing there that you and your husband, with your husband driving the car, along with your two children, got in the car and went up the street in the direction of where the gunshop was. Did you recall any incident of that kind? Mrs. Oswald. This is just a complete fabrication. Lee never drove a car with me. Only Ruth Paine drove a car with me. And I never took my baby with me. Mr. Rankin. Did you ever go into such a furniture store in Irving?
  • 56. Mrs. Oswald. Never. Mr. Rankin. That you recall? Mrs. Oswald. I was only twice in a store in Irving where they sell, like a cafe, where you can buy something to eat and where they sell toys and clothes and things like that; a little bit like a Woolworths, a one-story shop but without any furniture in it. Mr. Rankin. Do you know a Mrs. Whitworth who works in a furniture store in Irving? Mrs. Oswald. I was never in Irving in any furniture store. Mr. Rankin. Do you know a Mrs. Whitworth? Mrs. Oswald. It is the first time I have ever heard that name. Mr. Rankin. Do you know a Mrs. Hunter, a friend of Mrs. Whitworth? Mrs. Oswald. No. Mr. Rankin. Did you ever go on a trip with your husband to have a telescopic lens mounted on a gun at a gunshop? Mrs. Oswald. Never. No; this is all not true. In the first place, my husband couldn't drive, and I was never alone with him in a car. Anytime we went in a car it was with Ruth Paine, and there was never—we never went to any gun store and never had any telescopic lens mounted. Mr. Rankin. Did the four of you, that is, your husband, you, and your two children, ever go alone any place in Irving? Mrs. Oswald. In Irving the baby was only 1 month old. I never took her out anywhere. Representative Ford. Did you ever go anytime—— Mrs. Oswald. Just to doctor, you know. Representative Ford. Did you ever go anytime with your husband in a car with the rifle?
  • 57. Mrs. Oswald. I was never at anytime in a car with my husband and with a rifle. Not only with the rifle, not even with a pistol. Even without anything I was never with my husband in a car under circumstances where he was driving a car. Representative Ford. Did you go in a car with somebody else driving where your husband had the pistol or the rifle? Mrs. Oswald. Never. I don't know what to think about this. Mr. Rankin. Mrs. Oswald, I will hand you Commission's Exhibit No. 819 and ask you particularly about the signature at the bottom. Mrs. Oswald. That is Lee's handwriting, and this is mine. Mr. Rankin. Were the words A. J. Hidell, Chapter President on Commission Exhibit No. 819 are in your handwriting? Mrs. Oswald. Yes. Mr. Rankin. Would you tell the Commission how you happened to sign that? Mrs. Oswald. Lee wrote this down on a piece of paper and told me to sign it on this card, and said that he would beat me if I didn't sign that name on the card. Mr. Rankin. Did you have any other discussion about your signing that name? Mrs. Oswald. Yes. Mr. Rankin. What discussion did you have? Mrs. Oswald. I said that this sounded like Fidel. I said, You have selected this name because it sounds like Fidel and he blushed and said, Shut up, it is none of your business. Mr. Rankin. Was there any discussion about who Hidell, as signed on the bottom of that card, was? Mrs. Oswald. He said that it was his own name and a there is no Hidell in existence, and I asked him, You just have two names, and he said, Yes.
  • 58. Mr. Rankin. Was anything else said about that matter at any time? Mrs. Oswald. I taunted him about this and teased about this and said how shameful it is that a person who has his own perfectly good name should take another name and he said, It is none of your business, I would have to do it this way, people will think I have a big organization and so forth. Mr. Rankin. Did you ask him why he needed to have the other name in your handwriting rather than his own? Mrs. Oswald. I did ask him that and he would answer that in order that people will think it is two people involved and not just one. Mr. Dulles. Did you ever sign any more such cards with the name Hidell? Mrs. Oswald. Only this one. Mr. Dulles. And you never signed the name Hidell on any other paper at any time? Mrs. Oswald. Only once. Representative Ford. Where did this actual signing take place, Mrs. Oswald? Mrs. Oswald. In New Orleans. Representative Ford. Where in New Orleans? Mrs. Oswald. In what is the name of the street where we lived, in an apartment house. Representative Ford. In your apartment house? Mrs. Oswald. Yes; in our apartment house. Representative Ford. What time of day, do you recall? Mrs. Oswald. It might have been 8 or 9 o'clock in the evening. Mr. Dulles. Had you ever heard the name Hidell before?
  • 59. Mrs. Oswald. I don't remember whether this was before or after Lee spoke on the radio. I think it was after. Mr. Dulles. Did he use the name Hidell on the radio? Mrs. Oswald. I think that he might have when he was talking on the radio said that Hidell is the President of his organization but, of course, I don't understand English well and I don't know. He spoke on the radio using his own name but might have mentioned the name Hidell. This is what he told me. When I tried to find out what he said on the radio. Mr. Dulles. This might have been on television also? Mrs. Oswald. It was on the radio, not on television. He told me that someone had taken movies of him for to be shown later on television but I don't know if they ever were. Mr. Dulles. Did you ever sign the name Hidell at any subsequent time to any document? Mr. McKenzie. If you recall signing it. Do you recall signing his name to any other document? Mrs. Oswald. I only remember this one occasion. Mr. Rankin. Was the way you signed on this Commission's Exhibit No. 819 your usual way of writing English? Mrs. Oswald. My English handwriting changes every day, and my Russian handwriting, too. But that is more or less my usual style. Mr. Rankin. You weren't trying to conceal the way you sign anything? Mrs. Oswald. I tried to do it, I just tried to write it as nicely as possible. Mr. Dulles. Did you make some practice runs of writing this name before you actually put it on the card? Mrs. Oswald. Yes; because it was difficult for me to write English properly.
  • 60. Mr. Dulles. So you mean you wrote it several times on another sheet of paper and then put it on this card? Mrs. Oswald. Yes. Representative Ford. Was there anybody else present at the time of this incident? Mrs. Oswald. No; only Lee. Representative Ford. Did he have you sign only one card? Mrs. Oswald. This was the only time when I—when Lee asked me to do this and I did it. I might have signed two or—cards and not just one but there weren't a great many. Representative Ford. Did the other cards have someone else's name besides Lee Harvey Oswald on it? Mrs. Oswald. No; only Lee Oswald. Representative Ford. But you think you might have signed more than one such card? Mrs. Oswald. Maybe two, three. This is just 1 day when I was signing this. It just happened on one occasion. Mr. Rankin. Mrs. Oswald, turning to another subject, I would like to ask you about some correspondence with the Dallas Civil Liberties Union. Do you recall that they inquired as to whether you were being kept from seeing and speaking to people against your will? Mrs. Oswald. This letter was translated by Ruth Paine and I answered on the basis of the translation. Mr. McKenzie. May I see those letters, Mr. Rankin? Mr. Rankin. Yes. Mrs. Oswald. I didn't want to answer this letter. It was simply a matter of courtesy on my part.
  • 61. Mr. Rankin. Now, you received a letter from the local chapter of the Civil Liberties union in Russian, did you not? Mrs. Oswald. There was a letter that was in English and there was a translation which came with it, and it was stated that the translation was done by Ruth Paine. Mr. Rankin. What did you do with the translation or the—I will ask you the translation first. Did you keep that? Mrs. Oswald. I don't remember what I did with it. Mr. Rankin. Do you know what you did with the part that was in Russian? Mrs. Oswald. Perhaps it is somewhere among my papers but I didn't pay any special attention to it. Mr. Rankin. I will hand you Commission Exhibit No. 331 and ask you if that is the letter in English that you referred to? Mrs. Oswald. Yes; it is the letter. Mr. Rankin. I call the Commission's attention to the fact that that has already been received in evidence. Mr. McKenzie. Mr. Rankin, did you write Mr. Olds about this? This appears to be a letter in reply to a letter from you. Mr. Rankin. That is right. I asked for it. Mrs. Oswald, will you examine Commission Exhibits Nos. 990 and 991 and state whether you know the handwriting in these exhibits? Mrs. Oswald. This is all mine, my handwriting. This is the answer to that letter. Mr. Rankin. And the letter, Exhibit No. 990, and the envelope, Exhibit No. 991, in your handwriting were your response to the inquiry of the Dallas Civil Liberties Union on the Exhibit No. 331? Mrs. Oswald. Yes; this was my answer to this letter, Exhibit No. 331.
  • 62. Mr. Rankin. I offer in evidence Commission Exhibits Nos. 990 and 991. Mr. Dulles. You want them admitted at this time? Mr. Rankin. Yes; Mr. Chairman. Mr. Dulles. They shall be admitted. (Commission Exhibits Nos. 990 and 991 were marked for identification and received in evidence.) Mr. Rankin. Mrs. Oswald, I will ask you to examine Exhibit No. 988 and with the help of the interpreter, advise us whether or not it is a reasonably correct translation of your letter, Exhibit No. 990. Mrs. Oswald. This is not an accurate translation. Mr. Rankin. Mrs. Oswald, can you tell us what errors were made, where the corrections should be to make it a correct translation? Mrs. Oswald. There is one place here in which it refers to the third sentence of the English text which states: What you read in the papers is correct. Mr. Rankin. How would you correct that? Mrs. Oswald. This is incorrect. A better, a proper translation, although unofficial of this passage, and the Russian text of my letter would read, Your concern is quite unnecessary although it is quite understandable if one is to judge from what is written in the papers. Mr. Rankin. Now, will you proceed with any other corrections? Mrs. Oswald. This, the letter, the spirit of the letter reflects my own spirit in my own Russian text—although the translation is somewhat inaccurate and tends to shorten my own text somewhat. There is another inaccuracy which is more important than the others—it is not more important, the first one is more important— there is another which should be called to the Commission's attention.
  • 63. The last sentence of the English text reads: Please let Mrs. Ruth Paine know I owe to her much and think of her as one of my best friends. Whereas the letter only states that: Of course, consider her my friend. Mr. Rankin. Mrs. Oswald, I call your attention to Commission Exhibit No. 990 and ask you to note the date which appears to be December 7, 1964. The Dallas Civil Liberties Union letter, you will note, was dated January 6, 1964 which I will hand you so you can examine it. Could you explain that discrepancy? You might wish to examine them. Mrs. Oswald. It can't possibly be the 7th of December 1964 because it hasn't even come yet. Mr. Rankin. You might wish to examine the envelope, Exhibit No. 991, that may help you as to the correct date. Mrs. Oswald. January 8. I wrote this January 7. It was just my mistake. I wrote it on January 7 and mailed it on the 8th. I just out of habit still writing December. Mr. McKenzie. Mr. Rankin, may I ask the Commission, on Commission Exhibit No. 988, which purports to be a translation of Mrs. Oswald's letter to the Dallas Civil Liberties Union, do you know who translated this letter or could you tell us who translated the letter? Mr. Rankin. Mr. McKenzie—— Mrs. Oswald. They wrote me that I can answer them in Russian, and which I did but I haven't any idea who translated my answer. Mr. Rankin. The Commission Exhibit No. 987 which I will now offer states that the translation was handled by Mrs. Ford and later seen by Mrs. Paine. The translation of the exhibit that you now have in your hand, what is the number of that?
  • 64. Mr. McKenzie. This is Commission Exhibit No. 988 in English which purports to be a translation of Mrs. Oswald's letter to the Dallas Civil Liberties Union and I am asking does the Commission know who translated the letter? Mr. Rankin. We were informed by the Dallas Civil Liberties Union in Exhibit No. 987 that the translation was made by Mrs. Ford and later seen by Mrs. Paine, and I now offer all exhibits together with Exhibit No. 987 as part of the testimony of this witness. Mr. Dulles. The exhibits shall be admitted. Have we the numbers of all of these exhibits? Mr. Rankin. Yes; the reporter has them. (Commission Exhibit No. 987 was marked for identification and received in evidence.) Mr. Rankin. Mrs. Oswald, I will hand you the cameras of your—— Mr. Dulles. I wonder before we finish this—— Mr. McKenzie. I would prefer, Mr. Rankin, for the purposes of the record so that the record will be complete, to have a correct English translation of Mrs. Oswald's letter in the record in lieu of Commission Exhibit No. 988. Mr. Rankin. Mr. Chairman, if it is agreeable to the Commission, I would like to ask counsel to furnish such a translation and we will then make it the next number, Exhibit No. 992, as a part of this record. Mr. Dulles. That shall be admitted then as Exhibit No. 992, the other already being in the record I think, probably has to stay there particularly in view of all this discussion of it. Mr. Rankin. If you will furnish it. Mr. McKenzie. You are putting the onus or burden back on me, Mr. Rankin, when the Commission has a fully qualified, I presume, Russian interpreter here, and if the Commission would not mind
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