Most Cited Articles in Academia ---International Journal of Data Mining & Knowledge Management Process ( IJDKP )

Most Cited Articles in
Academia
International Journal of Data Mining & Knowledge Management Process
( IJDKP )
http://airccse.org/journal/ijdkp/ijdkp.html
ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print]

A REVIEW ON EVALUATION METRICS FOR DATA CLASSIFICATION
EVALUATIONS
Hossin M1
and Sulaiman M.N2
, 1
Universiti Malaysia Sarawak, Malaysia and 2
Universiti Putra
Malaysia, Malaysia
ABSTRACT
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less in
formativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative
metrics are also discussed. Finally, this paper suggests five important aspects that must be taken into
consideration in constructing a new discriminator metric.
KEYWORDS
Evaluation Metric, Accuracy, Optimized Classifier, Data Classification Evaluation
For More Details: http://aircconline.com/ijdkp/V5N2/5215ijdkp01.pdf
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AUTHORS
Mohammad b. Hossin is a senior lecturer at Universiti Malaysia Sarawak. He received his B.IT (Hons) from
Universiti Utara Malaysia (UUM) in 2000 and M.Sc. in Artificial Intelligence from University of Essex, UK in
2003. Then, in 2012, he received his Ph.D in Intelligent Computing from Universiti Putra Malaysia (UPM). His
main research interests include data mining, decision support systems optimization using nature-inspired algorithms
and e-learning.
Md Nasir Sulaiman received his Bachelor in Science with Education major in Mathematics from University
Pertanian Malaysia in 1983. He received Master in Computing from University of Bradford, U.K., in 1986 and PhD
degree in Computer Science from Loughborough University, U.K., in 1994. He is a lecturer at the University Putra
Malaysia since 1986. He has been promoted to Associate Professor in 2002. His research interests are Intelligent
Computing and Smart Home.

Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models
Nehal Mohamed Ali, Marwa Mostafa Abd El Hamid and Aliaa Youssif, Arab Academy for
Science Technology and Maritime, Egypt
ABSTRACT
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across
the internet and other media, Sentiment analysis has become vital for developing opinion mining systems.
This paper introduces a developed classification sentiment analysis using deep learning networks and
introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was
developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural
network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were
developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to
50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec
and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM
model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported
the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported
accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep
learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other
works using English datasets.
KEYWORDS
Deep learning, LSTM, CNN, Sentiment Analysis, Movies Reviews, Binary Classification

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Categorization of Factors Affecting Classification Algorithms Selection
Mariam Moustafa Reda, Mohammad Nassef and Akram Salah, Cairo University, Egypt
ABSTRACT
A lot of classification algorithms are available in the area of data mining for solving the same kind of
problem with a little guidance for recommending the most appropriate algorithm to use which gives best
results for the dataset at hand. As a way of optimizing the chances of recommending the most appropriate
classification algorithm for a dataset, this paper focuses on the different factors considered by data miners
and researchers in different studies when selecting the classification algorithms that will yield desired
knowledge for the dataset at hand. The paper divided the factors affecting classification algorithms
recommendation into business and technical factors. The technical factors proposed are measurable and
can be exploited by recommendation software tools.
KEYWORDS
Classification, Algorithm selection, Factors, Meta-learning, Landmarking
Volume Link: http://airccse.org/journal/ijdkp/vol9.html

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AUTHORS
Mariam Moustafa Reda received B.Sc. (2011) from Fayoum University, Cairo, Egypt in Computer
Science. In 2012, she joined IBM Egypt as Application Developer. Mariam has 2 published
patents. Since 2014, she started working in data analytics and classification related projects. Her
research interests include data mining methodologies improvement and automation.
Mohammad Nassef was graduated in 2003 from Faculty of Computers and Information, Cairo
University. He has got his M.Sc. degree in 2007, and his PhD in 2014 from the same University.
Currently, Dr Nassef is an Assistant Professor at the Department of Computer Science, Faculty of
Computers and Information, Cairo University, Egypt. He is interested in the research areas of
Bioinformatics, Machine Learning and Parallel Computing.
Akram Salah graduated from mechanical engineering and worked in computer programming for
7 years before he got his M.Sc. (85) and PhD degrees from the University of Alabama at
Birmingham, the USA in 1986 in computer and information sciences. He taught at the American
University in Cairo, Michigan State University, Cairo University, before he joined North Dakota
State University where he designed and started a graduate program that offers PhD and M.Sc. in
software engineering. Dr Salah’s research interest is in data knowledge and software engineering.
He has over 100 published papers. Currently, he is a professor in the Faculty of Computer and
Information, Cairo University. His current research is in knowledge engineering, ontology, semantics, and semantic
web.

Implementation of Risk Analyzer Model for Undertaking the Risk Analysis of
Proposed Building Projects for a Selected Client
Ibrahim Yakubu , Department of Quantity Surveying, Faculty of Environmental Design,
Abubakar Tafawa Balewa University, P.M,B. 0248, Bauchi, Bauchi State, Nigeria
ABSTRACT
The model of RISK ANALYZER was implemented as Knowledge-based System for the purpose of
undertaking risk analysis for proposed construction projects in a selected domain. The Fuzzy Decision
Variables (FDVs) that cause differences between initial and final contract sums of building projects were
identified, the likelihood of the occurrence of the risks were determined and a Knowledge-Based System
that would rank the risks was constructed using JAVA programming language and Graphic User
Interface. The Knowledge-Based System is composed a Knowledge Base for storing data, an Inference
Engine for controlling and directing the use of knowledge for problem-solution, and a User Interface that
assists the user retrieve, use and alter data in the Knowledge Base. The developed Knowledge-Based
System was compiled, implemented and validated with data of previously completed projects. The client
could utilize the Knowledge-Based System to undertake proposed building projects
KEYWORDS
RISK ANALYZER, Risk analysis, Knowledge-Based Systems, JAVA, Graphic User Interface

REFERENCES
[1] Bala, K. & Yakubu, I. (2008) “The Application of Fuzzy Decision Variables for Evaluating Risk Associated
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AUTHOR
Ibrahim Yakubu was born in Bauchi, Bauchi State, Nigeria. He attended Ahmadu Bello
University, Zaria, Nigeria; University of Jos, Nigeria and Abubakar Tafawa Balewa University,
Nigeria. He is a Registered Quantity Surveyor and a Professor of Construction Management at
Abubakar Tafawa Balewa University, Bauchi, Nigeria. His research specialization is in
Application of Information Technology to Construction Management. His hobbies include
reading, writing and travelling. He is married with children.

DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE
DISCOVERY PERSPECTIVE
Pratiyush Guleria and Manu Sood, Department of Computer Science, Himachal Pradesh
University, Shimla, Himachal Pradesh, India
ABSTRACT
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful
patterns from large databases and these patterns may then be converted into knowledge. Data mining is
the process of extracting the information and patterns derived by the KDD process which helps in crucial
decision-making. Data mining works with data warehouse and the whole process is divided into action
plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper,
we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of
data mining, its techniques and methods in it.
KEYWORDS
Decision, Knowledge, Mining, Selection, Transformation, Warehouse

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AUTHORS
Pratiyush Guleria is pursuing PhD in Computer Science from Himachal Pradesh University
Shimla, INDIA. He has done Mtech in Computer Science with a Gold Medal from Hi-machal
Pradesh University, Shimla, INDIA. He has received his MBA in Operation Research from
Indira Gandhi National Open University (IGNOU) and Btech in Information Technology from
I.E.E.T Baddi, Distt Solan, Himachal Pradesh University. He has more than 6 Years of
Experience in IT Industry and Academics. His research interests in-clude Data Mining and Web
Technologies.
Prof. Manu Sood is currently working as a Professor in the Department of Computer Science at
Himachal Pradesh University Shimla, India. He has completed his Ph.D. in Computer ngineering
under the Facul-ty of Technology from University of Delhi, Delhi, India. He completed his M.
Tech. in Information Systems with a Gold Medal from Netaji Subhash Institute of Technology,
Delhi, India. He has received his B.E. degree in Electronics and Telecommunication from
Government Engineering College, Jabalpur, Madhya Pradesh, India. Prof. Sood has over 25
years of extensive experience in IT Industry and Academics in India at various positions. His research interests
include Software Engi-neering, Model Driven Software Development, Model Driven Architec-ture, Aspect Oriented
Software Development, E-learning, Service Oriented Architecture, MANETs and VANETs

Increased Prediction Accuracy in The Game Of Cricketusing Machine
Learning
Kalpdrum Passi and Niravkumar Pandey, Department of Mathematics and Computer Science
Laurentian University, Sudbury, Canada
ABSTRACT
Player selection is one the most important tasks for any sport and cricket is no exception. The
performance of the players depends on various factors such as the opposition team, the venue, his current
form etc. The team management, the coach and the captain select 11 players for each match from a squad
of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best
playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler
contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the
performance of players as how many runs will each batsman score and how many wickets will each
bowler take for both the teams. Both the problems are targeted as classification problems where number
of runs and number of wickets are classified in different ranges. We used naïve bayes, random forest,
multiclass SVM and decision tree classifiers to generate the prediction models for both the problems.
Random Forest classifier was found to be the most accurate for both the problems.
KEYWORDS
Naïve Bayes, Random Forest, Multiclass SVM, Decision Trees, Cricket

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AUTHORS
Kalpdrum Passi received his Ph.D. in Parallel Numerical Algorithms from Indian Institute of
Technology, Delhi, India in 1993. He is an Associate Professor, Department of Mathematics &
Computer Science, at Laurentian University, Ontario, Canada. He has published many papers on
Parallel Numerical Algorithms in international journals and conferences. He has collaborative
work with faculty in Canada and US and the work was tested on the CRAY XMP’s and CRAY
YMP’s. He transitioned his research to web technology, and more recently has been involved in
machine learning and data mining applications in bioinformatics, social media and other data science areas. He
obtained funding from NSERC and Laurentian University for his research. He is a member of the ACM and IEEE
Computer Society.
Niravkumar Pandey is pursuing M.Sc. in Computational Science at Laurentian University,
Ontario, Canada. He received his Bachelor of Engineering degree from Gujarat Technological
University, Gujarat, India. Data mining and machine learning are his primary areas of interest. He
is also a cricket enthusiast and is studying applications of machine learning and data mining in
cricket analytics for his M.Sc. thesis.

Incremental Learning: Areas and Methods – A Survey
Prachi Joshi1
and Dr. Parag Kulkarni2
, 1Assistant Professor, MIT College of Engineering,
Pune and 2Adjunct Professor, College of Engineering, Pune
ABSTRACT
While the areas of applications in data mining are growing substantially, it has become extremely
necessary for incremental learning methods to move a step ahead. The tremendous growth of unlabeled
data has made incremental learning take up a big leap. Starting from BI applications to image
classifications, from analysis to predictions, every domain needs to learn and update. Incremental
learning allows to explore new areas at the same time performs knowledge amassing. In this paper we
discuss the areas and methods of incremental learning currently taking place and highlight its potentials
in aspect of decision making. The paper essentially gives an overview of the current research that will
provide a background for the students and research scholars about the topic.
KEYWORDS
Incremental, learning, mining, supervised, unsupervised, decision-making

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A BUSINESS INTELLIGENCE PLATFORM IMPLEMENTED IN A BIG
DATA SYSTEM EMBEDDING DATA MINING: A CASE OF STUDY
Alessandro Massaro, Valeria Vitti, Palo Lisco, Angelo Galiano and Nicola Savino,
Dyrecta Lab, IT Research Laboratory, Via Vescovo Simplicio, 45, 70014 Conversano
(BA), Italy.(in collaboration with ACI Global S.p.A., Viale Sarca, 336 - 20126 Milano,
Via Stanislao Cannizzaro, 83/a - 00156 Roma, Italy)
ABSTRACT
In this work is discussed a case study of a business intelligence –BI- platform developed within
the framework of an industry project by following research and development –R&D- guidelines
of ‘Frascati’. The proposed results are a part of the output of different jointed projects enabling
the BI of the industry ACI Global working mainly in roadside assistance services. The main
project goal is to upgrade the information system, the knowledge base –KB- and industry
processes activating data mining algorithms and big data systems able to provide gain of
knowledge. The proposed work concerns the development of the highly performing Cassandra big
data system collecting data of two industry location. Data are processed by data mining
algorithms in order to formulate a decision making system oriented on call center human
resources optimization and on customer service improvement. Correlation Matrix, Decision Tree
and Random Forest Decision Tree algorithms have been applied for the testing of the prototype
system by finding a good accuracy of the output solutions. The Rapid Miner tool has been
adopted for the data processing. The work describes all the system architectures adopted for the
design and for the testing phases, providing information about Cassandra performance and
showing some results of data mining processes matching with industry BI strategies.
KEYWORDS
Big Data Systems, Cassandra Big Data, Data Mining, Correlation Matrix, Decision Tree, Frascati
Guideline.

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AUTHOR
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.

Experimental study of Data clustering using k-Means and modified
algorithms
M. P. S Bhatia and Deepika Khurana, University of Delhi, India
ABSTRACT
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some
performance measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity
index and execution time.
KEYWORDS
Data Mining, Clustering Algorithm, k- Means, Silhouette Validity Index.

REFERENCES
[1] Ran Vijay Singh and M.P.S Bhatia , “Data Clustering with Modified K-means Algorithm”, IEEE International
Conference on Recent Trends in Information Technology, ICRTIT 2011, pp 717-721.
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AUTHORS
Dr. M.P.S Bhatia. Professor at Department of Computer Science & Engineering NETAJI
SUBASH INSTITUTE OF TECHNOLOGY, UNIVERSITY OF DELHI, INDIA.
Deepika Khurana Mtech. II year in Computer Science & Engineering (Information Systems) from
NETAJI SUBHASH INSTITUTE OF TECHNOLOGY, UNIVERSITY OF DELHI, INDIA

MACHINE LEARNING TECHNIQUES FOR ANALYSIS OF EGYPTIAN
FLIGHT DELAY
Shahinaz M. Al-Tabbakh 1
, Hanaa M. Mohamed2
and H. El-Zahed3
1
Computer Science Group, Faculty of Women for Sciences, A. and Education,
Ain Shames University, Cairo-Egypt.
2
Internet Dev.Dept. Manager of IT Sector-EGYPTAIR Holding Cooperation, Cairo, Egypt
3
Faculty of Women for Sciences, A. and Education, Ain Shames University, Cairo-Egypt.
ABSTRACT
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important
significance to research for computer system predicting flight delay propagation. Extraction of hidden
information from large datasets of raw data could be one of the ways for building predictive model. This
paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt
Airline’s Flight dataset. In this work, four decision tree classifiers were evaluated and results show that
the REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules
based classifiers were compared and results show that PART provides best accuracy among studied rule-
based classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work
concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also,
the current work is extended to apply of Apriori association technique to extract some important
information about flight delay. Association rules are presented and association technique is evaluated.
KEYWORDS
Airlines, Flight delay, WEKA, Bigdata, Data mining, classification Algorithms , J48,Random Forest,
Decision Stump, Ripper rule, Association rules, priori, Confusion matrix.

REFERENCE
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