CAP 4770: Introduction to Data Mining Fall 2010 Dr. Tao Li Florida International University

1. CAP 4770:Introduction to Data Mining Fall 2010Dr. Tao LiFlorida International University

2. Self-Introduction • Ph.D. from University of Rochester, 2004 • Associate Professor in the School of Computer Science at Florida International University • Research Interest • Data Mining • Machine Learning • Information Retrieval • Bioinformatics • Industry Experience: • Summer internships at Xerox Research (summer 2001, 2002) and IBM Research (Summer 2003, 2004) CAP 4770

3. My Research Projects • You can find on http://www.cis.fiu.edu/~taoli CAP 4770

4. Student Self-Introduction • Name • I will try to remember your names. But if you have a Long name, please let me know how should I call you  • Major and Academic status • Programming Skills • Java, C/C++, VB, Matlab, Scripts etc. • Anything you want us to know • e.g., I am a spurs fan.  CAP 4770

5. Acknowledgements • Some of the material used in this course is drawn from other sources: • Prof. Christopher W. Clifton at Purdue • Prof. Jiawei Han at UIUC • Profs. Pang-Ning Tan (Michigan State University), Michael Steinbach and Vipin Kumar (University of Minnesota) CAP 4770

6. Outline • Course Logistics • Data Mining Introduction • Four Key Characteristics • Combination of Theory and Application • Engineering Process • Collection of Functionalities • Interdisciplinary field • How do we categorize data mining systems? • History of Data Mining • Research Issues • Curse of Dimensionality CAP 4770

7. Course Overview • Meeting time • T/Th 2:00pm – 3:15pm • Office hours: • Tuesday 5:00pm – 6:00pm or by appointment • Course Webpage: • http://www.cs.fiu.edu/~taoli/class/CAP4770-F10/index.html • Lecture Notes and Assignments CAP 4770

8. Course Objectives This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection) CAP 4770

9. Assignments and Grading • Reading/Written Assignments • Research Projects • Midterm Exams • Final Project/Presentations • Class attendance is mandatory. • Evaluation will be a subjective process • Effort is very important component • Class Participation: 10% • Quizzes: 10% • Exams: 30% • Assignments: 50% • Final Project: 15% • Written Homework: 15% • Other Projects: 20% CAP 4770

10. Text and References • Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. • Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. CAP 4770

11. Outline • Course Logistics • Data Mining Introduction • Four Key Characteristics • Combination of Theory and Application • Engineering Process • Collection of Functionalities • Interdisciplinary field • How do we categorize data mining systems? • History of Data Mining • Research Issues • Curse of Dimensionality CAP 4770

12. Why Data Mining? • Motivation: “Necessity is the Mother of Invention” • Data explosion problem • Applications generate huge amounts of data • WWW, computer systems/programs, biology experiments, Business transactions, Scientific computation and simulation, Medical and person data, Surveillance video and pictures, Satellite sensing, Digital media, • Technologies are available to collect and store data • Bar codes, scanners, satellites, cameras etc. • Databases, data warehouses, variety of repositories … • We are drowning in data, but starving for knowledge! CAP 4770

13. What Is Data Mining? • Data mining (knowledge discovery from data) • Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data • What is not data mining? • (Deductive) query processing. • Expert systems or small ML/statistical programs • Key Characteristics • Combination of Theory and Application • Engineering Process • Data Pre-processing and Post-processing, Interpretation • Collection of Functionalities • Different Tasks and Algorithms • Interdisciplinary Field CAP 4770

14. Real Example from NBA • AS (Advanced Scout) software from IBM Research • Coach can assess the effectiveness of certain coaching decisions • Good/bad player matchups • Plays that work well against a given team • Raw Data: Play-by-play information recorded by teams • Who is on court • Who took a shot, the type of shot, the outcome, any rebounds CAP 4770

15. Starks+Houston+ Ward playing AS Knowledge Discovery • Text Description • When Price was Point-Guard, J. Williams made 100% of his jump field-goal-attempts. The total number of such attempts is 4. • Graph Description Reference: Bhabdari et al. Advanced Scout: Data Mining and Knowledge Discovery in NBA Data. Data Mining and Knowledge Discovery, 1, 121-125(1997) CAP 4770

16. Ads vs. search results

17. Ads vs. search results • Search advertising is the revenue model • Multi-billion-dollar industry • Advertisers pay for clicks on their ads • Interesting problems • What ads to show for a search? • If I’m an advertiser, which search terms should I bid on and how much to bid?

18. Outline • Course Logistics • Data Mining Introduction • Four Key Characteristics • Combination of Theory and Application • Engineering Process • Collection of Functionalities • Interdisciplinary field • How do we categorize data mining systems? • History of Data Mining • Research Issues • Curse of Dimensionality CAP 4770

19. Potential Applications • Data analysis and decision support • Market analysis and management • Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation • Risk analysis and management • Forecasting, customer retention, improved underwriting, quality control, competitive analysis • Fraud detection and detection of unusual patterns (outliers) • Other Applications • Text mining (news group, email, documents) and Web mining • Stream data mining • System and Network Management • Multimedia Applications • Music, Image, Video • DNA and bio-data analysis CAP 4770

20. Example: Use in retailing • Goal: Improved business efficiency • Improve marketing (advertise to the most likely buyers) • Inventory reduction (stock only needed quantities) • Information source: Historical business data • Example: Supermarket sales records • Size ranges from 50k records (research studies) to terabytes (years of data from chains) • Data is already being warehoused • Sample question – what products are generally purchased together? • The answers are in the data, if only we could see them CAP 4770

21. Other Applications • Network System management • Event Mining Research at IBM • Astronomy • JPL and the Palomar Observatory discovered 22 quasars with the help of data mining • Internet Web Surf-Aid • IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc. CAP 4770

22. Market Analysis and Management (1) • Where are the data sources for analysis? • Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies • Target marketing • Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. • Determine customer purchasing patterns over time • Conversion of single to a joint bank account: marriage, etc. • Cross-market analysis • Associations/co-relations between product sales • Prediction based on the association information CAP 4770

23. Market Analysis and Management (2) • Customer profiling • data mining can tell you what types of customers buy what products (clustering or classification) • Identifying customer requirements • identifying the best products for different customers • use prediction to find what factors will attract new customers • Provides summary information • various multidimensional summary reports • statistical summary information (data central tendency and variation) CAP 4770

24. Corporate Analysis and Risk Management • Finance planning and asset evaluation • cash flow analysis and prediction • contingent claim analysis to evaluate assets • cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) • Resource planning: • summarize and compare the resources and spending • Competition: • monitor competitors and market directions • group customers into classes and a class-based pricing procedure • set pricing strategy in a highly competitive market CAP 4770

25. Fraud Detection and Management (1) • Applications • widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. • Approach • use historical data to build models of fraudulent behavior and use data mining to help identify similar instances • Examples • auto insurance: detect a group of people who stage accidents to collect on insurance • money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) • medical insurance: detect professional patients and ring of doctors and ring of references CAP 4770

26. Fraud Detection and Management (2) • Detecting inappropriate medical treatment • Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr). • Detecting telephone fraud • Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm. • British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. • Retail • Analysts estimate that 38% of retail shrink is due to dishonest employees. CAP 4770

27. Outline • Course Logistics • Data Mining Introduction • Four Key Characteristics • Combination of Theory and Application • Engineering Process • Collection of Functionalities • Interdisciplinary field • How do we categorize data mining systems? • History of Data Mining • Research Issues • Curse of Dimensionality CAP 4770

28. Interpretation/ Evaluation Mining Algorithms Preprocessing Patterns Selection Preprocessed Data Data Target Data Data Mining: An Engineering Process • Data mining: interactive and iterative process. Knowledge adapted from: U. Fayyad, et al. (1995), “From Knowledge Discovery to Data Mining: An Overview,” Advances in Knowledge Discovery and Data Mining, U. Fayyad et al. (Eds.), AAAI/MIT Press CAP 4770

29. Steps of a KDD Process • Learning the application domain • relevant prior knowledge and goals of application • Creating a target data set: data selection • Data cleaning and preprocessing: (may take 60% of effort!) • Data reduction and transformation • Find useful features, dimensionality/variable reduction, invariant representation. • Choosing functions of data mining • summarization, classification, regression, association, clustering. • Choosing the mining algorithm(s) • Data mining: search for patterns of interest • Pattern evaluation and knowledge presentation • visualization, transformation, removing redundant patterns, etc. • Use of discovered knowledge CAP 4770

30. Outline • Course Logistics • Data Mining Introduction • Four Key Characteristics • Combination of Theory and Application • Engineering Process • Collection of Functionalities • Interdisciplinary field • How do we categorize data mining systems? • History of Data Mining • Research Issues • Curse of Dimensionality CAP 4770

31. Architecture of a Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge-base Database or data warehouse server Filtering Data cleaning & data integration Data Warehouse Databases CAP 4770

32. Data Mining: On What Kind of Data? • Relational databases • Data warehouses • Transactional databases • Advanced DB and information repositories • Object-oriented and object-relational databases • Spatial databases • Time-series data and temporal data • Text databases and multimedia databases • Heterogeneous and legacy databases • WWW CAP 4770

33. What Can Data Mining Do? • Cluster • Classify • Categorical, Regression • Semi-supervised • Summarize • Summary statistics, Summary rules • Link Analysis / Model Dependencies • Association rules • Sequence analysis • Time-series analysis, Sequential associations • Detect Deviations CAP 4770

34. 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. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 CAP 4770

35. Data Mining Tasks... • Classification [Predictive] • Clustering [Descriptive] • Association Rule Discovery [Descriptive] • Sequential Pattern Discovery [Descriptive] • Regression [Predictive] • Deviation Detection [Predictive] CAP 4770

36. Test Set Model Classification Example categorical categorical continuous class Learn Classifier Training Set CAP 4770

37. Classification: Definition • 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. CAP 4770

38. Classification: Application 1 • Direct Marketing • Goal: Reduce 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. From [Berry & Linoff] Data Mining Techniques, 1997 CAP 4770

39. 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. CAP 4770

40. Classification: Application 3 • Customer Attrition/Churn: • Goal: To predict whether a customer is likely to be lost to a competitor. • Approach: • Use detailed record of transactions with each of the past and present customers, to find attributes. • How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc. • Label the customers as loyal or disloyal. • Find a model for loyalty. From [Berry & Linoff] Data Mining Techniques, 1997 CAP 4770

41. Classification: Application 4 • Sky Survey Cataloging • Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory). • 3000 images with 23,040 x 23,040 pixels per image. • Approach: • Segment the image. • Measure image attributes (features) - 40 of them per object. • Model the class based on these features. • Success Story: Could find 16 new high red-shift quasars, some of the farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 CAP 4770

42. Clustering Definition • 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 one 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 Problem-specific Measures. CAP 4770

43. Illustrating Clustering • Euclidean Distance Based Clustering in 3-D space. Intracluster distances are minimized Intercluster distances are maximized CAP 4770

44. Clustering: Application 1 • 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. CAP 4770

45. Clustering: Application 2 • Document Clustering: • Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. • Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. • Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents. CAP 4770

46. Clustering of S&P 500 Stock Data • Observe Stock Movements every day. • Clustering points: Stock-{UP/DOWN} • Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day. • We used association rules to quantify a similarity measure. CAP 4770

47. Association Rule Discovery: Definition • 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. Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} CAP 4770

48. Association Rule Discovery: Application 1 • Marketing and Sales Promotion: • Let the rule discovered be {Bagels, … } --> {Potato Chips} • Potato Chipsas consequent => Can be used to determine what should be done to boost its sales. • Bagels in the antecedent => Can be used to see which products would be affected if the store discontinues selling bagels. • Bagels in antecedentandPotato chips in consequent=> Can be used to see what products should be sold with Bagels to promote sale of Potato chips! CAP 4770

49. Association Rule Discovery: Application 2 • 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! CAP 4770

50. Association Rule Discovery: Application 3 • 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. CAP 4770