![Data Mining Tasks
• Prediction Methods
– Use some variables to predict unknown or future values of other variables.
• Description Methods
– Find human-interpretable patterns that describe the data.
• Data Mining Tasks
– Classification [Predictive]
– Clustering [Descriptive]
– Association Rule Discovery [Descriptive]
– Sequential Pattern Discovery [Descriptive]
– Regression [Predictive]
– Deviation Detection [Predictive]
Data Mining 18](https://image.slidesharecdn.com/module-1-introductiontodatamining-250702083904-3df0208f/85/Module-1-IntroductionToDataMining-Data-Mining-18-320.jpg)
![Association Rule Discovery
• Given a set of records each of which contain some number of items from a given
collection;
– Produce dependency rules which will predict occurrence of an item based on
occurrences of other items.
• Frequent patterns (or frequent itemsets)
– What items are frequently purchased together in a store?
• A typical association rule
– Diaper Beer [0.5%, 75%] (support, confidence)
• How to mine such patterns and rules efficiently in large datasets?
• How to use such patterns for classification, clustering, and other applications?
Data Mining 33](https://image.slidesharecdn.com/module-1-introductiontodatamining-250702083904-3df0208f/85/Module-1-IntroductionToDataMining-Data-Mining-33-320.jpg)
Module-1-IntroductionToDataMining (Data Mining)
- 2. Introduction to Data Mining Data Mining 2
- 3. Why do we need Data Mining? • Explosive Growth of Data – Amount of data collected increasing exponentially – Automated data collection tools, database systems, Web, computerized society – Business: Web, e-commerce, transactions, stocks, purchases at grocery stores, Bank/Credit Card transactions – Science: remote sensors on a satellite, telescopes scanning skies, microarrays generating gene expression data, – Society and everyone: news, digital cameras, YouTube. Facebook, web • We have mass amount of data, but we need knowledge. • There is often information (knowledge) “hidden” in the data that is not readily evident • Human analysts may take weeks to discover useful information, • Much of the data is never analyzed Data Mining 3
- 4. Why do we need Data Mining? • Huge volumes of data are accumulated in databases and data warehouses. • Huge volumes of data also come from WWW and data streams – (video surveillance, telecommunication, and sensor networks) • Effective and efficient analysis of data in different forms becomes a challenging task. • Fast-growing, tremendous amount of data, collected and stored in large and numerous data repositories, has far exceeded our human ability for comprehension without powerful tools • Decision-making is done according to information (not data) Data Mining 4 We are data rich, but information poor
- 5. What is Data Mining? • Many Definitions for Data Mining: – Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns – Extracting or “mining” knowledge from large amounts of data. • Alternative Names for Data Mining – Knowledge Discovery from Data (KDD) – Knowledge Discovery (mining) in Databases, knowledge extraction, data/pattern analysis, data archeology, information harvesting, business intelligence, etc Data Mining 5
- 6. What is (not) Data Mining? • Is everything “data mining”? • What is not Data Mining? – Simple search and query processing – (Deductive) expert systems – Look up phone number in phone directory – Query a Web search engine for information about “Amazon • What is Data Mining? – Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) – Which items are bought together in a market? Data Mining 6
- 7. What is Data Mining: Tasks 1 Discuss whether or not each of the following activities is a data mining task? • Dividing the customers of a company according to their gender. – If their genders are recorded in data. – If their genders are NOT recorded in data. • Computing the total sales of a company. • Sorting a student database based on student identification numbers. Data Mining 7
- 8. What is Data Mining?: Tasks 1 Discuss whether or not each of the following activities is a data mining task? • Dividing the customers of a company according to their gender. – If their genders are recorded in data. NO – If their genders are NOT recorded in data. YES • Computing the total sales of a company. NO • Sorting a student database based on student identification numbers. NO Data Mining 8
- 9. What is Data Mining?: Tasks 2 Discuss whether or not each of the following activities is a data mining task? • Predicting the outcomes of tossing a fair coin. • Predicting the future stock price of a company using historical records. • Monitoring the heart rate of a patient for abnormalities. Data Mining 9
- 10. What is Data Mining?: Tasks 2 Discuss whether or not each of the following activities is a data mining task? • Predicting the outcomes of tossing a fair coin. NO • Predicting the future stock price of a company using historical records. YES • Monitoring the heart rate of a patient for abnormalities. YES Data Mining 10
- 11. What is Data Mining?: Tasks 3 Discuss whether or not each of the following activities is a data mining task? • Finding the category (economy, sport, …) of a newspaper article • Deciding whether an email is a spam or not. • Deciding whether an image contains an apple or not. Data Mining 11
- 12. What is Data Mining?: Tasks 3 Discuss whether or not each of the following activities is a data mining task? • Finding the category (economy, sport, …) of a newspaper article. YES • Deciding whether an email is a spam or not. YES • Deciding whether an image contains an apple or not. YES Data Mining 12
- 13. Origins of Data Mining • Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems, … Data Mining 13 Applications Data Mining Machine Learning Statistics Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology
- 14. Knowledge Discovery (KDD) Process • A view from typical database systems and data warehousing communities • Data mining plays an essential role in the knowledge discovery process Data Mining 14 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
- 15. Data Mining in Business Intelligence Data Mining 15
- 16. KDD Process: A Typical View from ML and Statistics Data Mining 16 Input Data Data Mining Data Pre- Processing Post- Processing Data integration Normalization Feature selection Dimension reduction Pattern discovery Association & correlation Classification Clustering Outlier analysis … … … … Pattern evaluation Pattern selection Pattern interpretation Pattern visualization
- 17. Data Mining: On What Kinds of Data? • Database-oriented data sets and applications – Relational database, data warehouse, transactional database – Object-relational databases, Heterogeneous databases • Advanced data sets and advanced applications – Data streams and sensor data – Time-series data, temporal data, sequence data (incl. bio-sequences) – Structure data, graphs, social networks and information networks – Spatial data and spatiotemporal data – Multimedia database – Text databases – The World-Wide Web Data Mining 17
- 18. Data Mining Tasks • Prediction Methods – Use some variables to predict unknown or future values of other variables. • Description Methods – Find human-interpretable patterns that describe the data. • Data Mining Tasks – Classification [Predictive] – Clustering [Descriptive] – Association Rule Discovery [Descriptive] – Sequential Pattern Discovery [Descriptive] – Regression [Predictive] – Deviation Detection [Predictive] Data Mining 18
- 19. Classification • Given a collection of records ( training set ) – Each record contains a set of attributes, – One of the attributes is the class. • Find a model for class attribute as a function of the values of other attributes. • Goal: previously unseen records should be assigned a class as accurately as possible. – A test set is used to determine the accuracy of the model. – Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. Data Mining 19
- 20. Classification: Typical Methods & Applications • Typical methods: – Decision trees, – Naïve Bayesian classification, – support vector machines, – neural networks, – rule-based classification, – pattern-based classification, … • Typical applications: – Credit card fraud detection, direct marketing, – Classifying diseases, web-pages, Data Mining 20
- 21. Classification: Typical Methods Data Mining 21
- 22. Classification: Example Data Mining 22 Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 Refund Marital Status Taxable Income Cheat No Single 75K ? Yes Married 50K ? No Married 150K ? Yes Divorced 90K ? No Single 40K ? No Married 80K ? 10 Test Set Training Set Model Learn Classifier
- 23. Classification: Application 1 Direct Marketing Goal: – Reduce the cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. Approach: – Use the data for a similar product introduced before. – We know which customers decided to buy and which decided otherwise. • This {buy, don’t buy} decision forms the class attribute. – Collect various demographic, lifestyle, and company-interaction related information about all such customers. • Type of business, where they stay, how much they earn, etc. – Use this information as input attributes to learn a classifier model. Data Mining 23
- 24. Classification: Application 2 Fraud Detection Goal: – Predict fraudulent cases in credit card transactions. Approach: – Use credit card transactions and the information on its account-holder as attributes. • When does a customer buy, what does he buy, how often he pays on time, etc. – Label past transactions as fraud or fair transactions. • This forms the class attribute. – Learn a model for the class of the transactions. – Use this model to detect fraud by observing credit card transactions on an account. Data Mining 24
- 25. Classification – Example Text Categorization • How can we classify a given newspaper text as an economy, sport or health article? Data Mining 25
- 26. Classification – Example Text Categorization • How can we classify a given newspaper text as an economy, sport or health article? • Create a training set. – Collect articles whose categories are known as economy, sport and health. For example, 100 articles from each category. • Create a test set. – Similarly, collect articles whose categories are known as economy, sport and health to create a test set. Data Mining 26
- 27. Classification – Example Text Categorization • How can we classify a given newspaper text as an economy, sport or health article? • Create a training set. – Collect articles whose categories are known as economy, sport and health. For example, 100 articles from each category. • Create a test set. – Similarly, collect articles whose categories are known as economy, sport and health to create a test set. • Determine the attributes: – Possible attributes are words appearing in texts (maybe not all words). – Words appearing more frequently in one category are good candidates as attributes. Data Mining 27
- 28. Classification – Example Text Categorization • How can we classify a given newspaper text as an economy, sport or health article? • Create a training set. – Collect articles whose categories are known as economy, sport and health. For example, 100 articles from each category. • Create a test set. – Similarly, collect articles whose categories are known as economy, sport and health to create a test set. • Determine the attributes: – Possible attributes are words appearing in texts (maybe not all words). – Words appearing more frequently in one category are good candidates as attributes. • Decide the classification method: decision tree, naïve Bayes, svm, … • Create the model using the selected classification method. Data Mining 28
- 29. Classification – Example Text Categorization • How can we classify a given newspaper text as an economy, sport or health article? • Create a training set. – Collect articles whose categories are known as economy, sport and health. For example, 100 articles from each category. • Create a test set. – Similarly, collect articles whose categories are known as economy, sport and health to create a test set. • Determine the attributes: – Possible attributes are words appearing in texts (maybe not all words). – Words appearing more frequently in one category are good candidates as attributes. • Decide the classification method: decision tree, naïve Bayes, svm, … • Create the model using the selected classification method. • Test the accuracy of the created model. Data Mining 29
- 30. Clustering • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in each cluster are more similar to one another. – Data points in separate clusters are less similar to one another. • Similarity Measures: – Euclidean Distance if attributes are continuous. – Other Similarity Measures: Problem-specific measures. • Unsupervised learning (i.e., Class label is unknown) • Group data to form new categories (i.e., clusters), • Principle: Maximizing intra-class similarity & minimizing interclass similarity • Many methods and applications Data Mining 30
- 31. Euclidean Distance Based Clustering in 3-D space Data Mining 31 Intracluster distances are minimized Intercluster distances are maximized
- 32. Clustering: Application Market Segmentation Goal: – Subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Approach: – Collect different attributes of customers based on their geographical and lifestyle related information. – Find clusters of similar customers. – Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. Data Mining 32
- 33. Association Rule Discovery • Given a set of records each of which contain some number of items from a given collection; – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. • Frequent patterns (or frequent itemsets) – What items are frequently purchased together in a store? • A typical association rule – Diaper Beer [0.5%, 75%] (support, confidence) • How to mine such patterns and rules efficiently in large datasets? • How to use such patterns for classification, clustering, and other applications? Data Mining 33
- 34. Association Rule Discovery: Application 1 Supermarket shelf management Goal: – To identify items that are bought together by sufficiently many customers. Approach: – Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule: – If a customer buys diaper and milk, then he is very likely to buy beer. – So, don’t be surprised if you find six-packs stacked next to diapers! Data Mining 34
- 35. Association Rule Discovery: Application 2 Inventory Management Goal: – A consumer appliance repair company wants to anticipate the nature of repairs on its consumer products and keep the service vehicles equipped with right parts to reduce on number of visits to consumer households. Approach: – Process the data on tools and parts required in previous repairs at different consumer locations and discover the co-occurrence patterns. Data Mining 35
- 36. Sequential Pattern Discovery • Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. • Rules are formed by first discovering patterns. • Event occurrences in the patterns are governed by timing constraints. Data Mining 36 (A B) (C) (D E) (A B) (C) (D E)
- 37. Sequential Pattern Discovery: Examples In telecommunications alarm logs: (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) (Fire_Alarm) In point-of-sale transaction sequences: • Computer Bookstore: (Intro_To_Visual_C) (C++_Primer) (Perl_for_dummies,Tcl_Tk) • Athletic Apparel Store: (Shoes) (Racket, Racketball) (Sports_Jacket) Data Mining 37
- 38. Regression • Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. • Greatly studied in statistics, neural network fields. • Examples: – Predicting sales amounts of new product based on advertising expenditure. – Predicting wind velocities as a function of temperature, humidity, air pressure, etc. – Time series prediction of stock market indices. Data Mining 38
- 39. Deviation/Anomaly Detection • Detect significant deviations from normal behavior • Applications: – Credit Card Fraud Detection – Network Intrusion Detection • Outlier analysis – Outlier: A data object that does not comply with the general behavior of the data – Noise or exception? • One person’s garbage could be another person’s treasure – Methods: by product of clustering or regression analysis, … – Useful in fraud detection, rare events analysis Data Mining 39
- 40. Structure and Network Analysis • Graph mining – Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) • Information network analysis – Social networks: actors (objects, nodes) and relationships (edges) • e.g., author networks in CS, terrorist networks – Multiple heterogeneous networks • A person could be multiple information networks: friends, family, classmates, – Links carry a lot of semantic information: Link mining • Web mining – Web is a big information network: from PageRank to Google – Analysis of Web information networks • Web community discovery, opinion mining, usage mining, … Data Mining 40
- 41. Evaluation of Knowledge • Are all mined knowledge interesting? – One can mine tremendous amount of “patterns” – Some may fit only certain dimension space (time, location, …) – Some may not be representative, may be transient, … • Evaluation of mined knowledge → directly mine only interesting knowledge? – Descriptive vs. predictive – Coverage – Typicality vs. novelty – Accuracy – Timeliness Data Mining 41
- 42. Major Issues in Data Mining • Mining Methodology – Mining various and new kinds of knowledge – Mining knowledge in multi-dimensional space – Data mining: An interdisciplinary effort – Boosting the power of discovery in a networked environment – Handling noise, uncertainty, and incompleteness of data – Pattern evaluation and pattern- or constraint-guided mining • User Interaction – Interactive mining – Incorporation of background knowledge – Presentation and visualization of data mining results Data Mining 42
- 43. Major Issues in Data Mining • Efficiency and Scalability – Efficiency and scalability of data mining algorithms – Parallel, distributed, stream, and incremental mining methods • Diversity of data types – Handling complex types of data – Mining dynamic, networked, and global data repositories • Data mining and society – Social impacts of data mining – Privacy-preserving data mining – Invisible data mining Data Mining 43
- 44. Summary – Data Mining Overview • Data mining: Discovering interesting patterns and knowledge from massive amount of data • A natural evolution of science and information technology, in great demand, with wide applications • A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation • Mining can be performed in a variety of data • Data mining functionalities: characterization, discrimination, association, classification, clustering, trend and outlier analysis, etc. • Data mining technologies and applications • Major issues in data mining Data Mining 44
- 45. Software • The software required for this course (Weka and R/RStudio) are open source and freely available under GNU General Public License. • Depending on your system, you can download and install Weka 3.8 which is the lasted stable version of Weka available at http://www.cs.waikato.ac.nz/ml/weka/downloading.html. • In addition, most recent version of R and RStudio (IDE for R) are available at https://cran.rstudio.com/ and https://www.rstudio.com/products/rstudio/download/, respectively. Data Mining 45
- 46. Weka A simple training set (.csv file) holding our data. This file can be loaded by Weka. Data Mining 46 Outlook Temp Humidity Wind PlayTennis Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Weak Yes Sunny Mild High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Strong Yes Sunny Mild Normal Strong Yes Overcast Mild High Strong Yes Overcast Hot Normal Weak Yes Rain Mild High Strong No
- 49. Reference Books • Introduction to Data Mining, PangNing Tan, Michael Steinbach, Vipin Kumar, 3rd Edition, Pearson, 2014. • Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2012. • Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank and Mark A. Hall, 4th Edition, Morgan Kaufmann, 2017. • Mining of Massive Datasets, Anand Rajaraman & Jeffrey D. Ullman, 2011 • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009 • B. Liu, Web Data Mining, Springer 2006 Data Mining 49