Introduction to Data Mining (these slides are based on a variety of sources)

1. Introduction to Data Mining (these slides are based on a variety of sources) CISC 4631: Data Mining Fall 2010

2. Let’s Start By Seeing What you Know CISC 4631: Data Mining • Quick Quiz • Do you know what Data Mining is? • Do you know of any examples of Data Mining?

3. What is Data Mining? • Data Mining has many definitions • 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 CISC 4631: Data Mining

4. Alternative Names • Data Mining also known as or related to: • Knowledge discovery in databases (KDD) • Knowledge extraction • Data/pattern analysis • Data archeology, data dredging, information harvesting, business intelligence, etc. CSRU4631: Data Mining

5. Some Examples CISC 4631: Data Mining • Netflix and Amazon use data mining to recommend products (recommender systems) • Companies use data mining for marketing • Who should be mailed a catalog • Who should see what online ads (Google Adwords) • My WISDM project uses data mining to determine (from your cell phone accelerometer data) who you are and what you are doing

6. Why Data Mining and Why Now? CISC 4631: Data Mining • Data Mining was not very popular until the last 10 years or so. • Quick Quiz: • Why is it data mining popular now? • What changed?

7. Why Mine Data? • There are now tremendous amounts of data that are automatically collected and warehoused. What are some examples? • Web data, e-commerce • Store purchases • Bank/Credit Card transactions • Cell phone GPS information CISC 4631: Data Mining

8. Why Mine Data? CISC 4631: Data Mining • What technological changes have helped make data mining so prevalent now? • Computers: cheaper and more powerful • Smaller mobile devices are exploding in popularity • Disk and other storage: greater capacity and cheaper • RFID (radio frequency IDs), bar codes, etc • Increased use of on-line resources and Internet

9. Why Mine Data? CISC 4631: Data Mining • In business, competitive pressure is strong • Provide better, customized services for an edge (e.g. in Customer Relationship Management) • CRM is a relatively big deal now • How do we get the most out of the customer over the long run • Example: Customer Churn Analysis

10. Scientific Viewpoint CISC 4631: Data Mining • Data collected at enormous speeds • remote sensors on satellite • telescopes scanning the skies • microarrays generating gene expression data • scientific simulations • Traditional techniques infeasible • Data mining may help scientists • in classifying and segmenting data • in hypothesis formation

11. Mining Large Data Sets - Motivation • Often information “hidden” in data that is not evident • Human analysts may take weeks to discover useful info • Much of the data is never analyzed at all The Data Gap Total new disk (TB) since 1995 # of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications” CISC 4631: Data Mining

12. Mining Large Data Sets - Motivation • AT&T’s 26TB call detail database (2003) • Ebay 6PB, IRS 150TB data warehouse • Yahoo has a 2PB DB to analyze behavior of ½ billion web visitors/month (24 billion events/day) • Wal-Mart has a 583 TB database (2006) • Indexed web contains about 20 Billion pages • Sites like Facebook, Flicker & Twitter contain lots of data CISC 4631: Data Mining

13. Data Deluge hospital patient registries hospital patient registries hospital patient registries hospital patient registries hospital patient registries hospital patient registries electronic point-of-sale data electronic point-of-sale data electronic point-of-sale data electronic point-of-sale data electronic point-of-sale data electronic point-of-sale data remote sensing images tax returns remote sensing images tax returns remote sensing images tax returns stock trades OLTP telephone calls stock trades OLTP telephone calls stock trades OLTP telephone calls stock trades OLTP telephone calls stock trades OLTP telephone calls airline reservations credit card charges airline reservations credit card charges catalog orders bank transactions catalog orders bank transactions catalog orders bank transactions catalog orders bank transactions CISC 4631: Data Mining

14. Amount of Data Created in One Year • Humans created/copied 161/281 Exabytes in 06/07 (IDC) • 1 Exabyte = 1018 • 12 stacks of books stretching from Earth to Sun • 3 million times the books ever written • In 2010 will be 988 Exabytes • Not all data stored at once • Much only temporarily • UC Berkeley 2003 estimate: • 5 Exabytesof new data created in 2002 • US produces ~40% of new stored data worldwide CISC 4631: Data Mining

15. Data Growth Rate • Twice as much information was created in 2002 as in 1999 (~30% growth rate) • Other growth rate estimates even higher • Very little data will ever be looked at by a human • Knowledge Discovery is NEEDED to make sense and use of data • Moore’s Law: • The information density on silicon-integrated circuits doubles every 18 to 24 months. • Parkinson’s Law: • Work expands to fill the time available for its completion CISC 4631: Data Mining

16. Origins of Data Mining • Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems • Traditional techniquesmay be unsuitable due to • Enormity of data • High dimensionality of data • Heterogeneous, distributed nature of data Artificial Intelligence / Machine Learning/ Pattern Recognition Statistics Data Mining Database systems CISC 4631: Data Mining

17. Origins of Data Mining: My view • Biggest contributor is Machine Learning, which is a subfield of Artificial Intelligence • Data Mining is a subset of machine learning and focuses on practical problems of learning from data • Unlike machine learning, ultimate goal is not to build something that can learn as flexibly as a human • Does include other data analysis methods, like statistics • Databases do not play a central role in data mining. • Most DM does not occur on data in a conventional database, but rather extracts it to a flat file. • Data Mining methods do not work while data in a conventional (relational) database. CISC 4631: Data Mining

18. Statistics & Machine Learning vs. Data Mining • When compared to Data Mining: • Statistics is: • more theory-based/based on mathematics as opposed to heuristic methods • more focused on testing hypotheses • makes more assumptions about the data • Machine learning is: • focused on improving performance of a learning agent in an environment CISC 4631: Data Mining

19. The KDD (Data Mining) Process Data Mining is a process, sometimes referred to as a knowledge discovery process. In this process there is a data mining step that applies data mining algorithms to extract knowledge. About 80% of our class in on the data mining step. CISC 4631: Data Mining

20. Back to “What is a Data Mining”? • My opinion: • Before determining whether something is data mining need to consider: • Is it a DM task? • Is it implemented using a DM method? • Ideally, both parts will use data mining but may be considered DM even if only is used for one. • We now will list the key DM tasks • The course is organized around these tasks CISC 4631: Data Mining

21. Second Part of Introduction: Data Mining Tasks CISC 4631: Data Mining

22. 2 Top Level Data Mining Tasks • At highest level, data mining tasks can be divided into: • Prediction Tasks • Use some variables to predict unknown or future values of other variables • Description Tasks • Find human-interpretable patterns that describe the data CISC 4631: Data Mining

23. Key Data Mining Tasks • Overview of major data mining tasks: • Predictive • Classification • Regression • Deviation/Anomaly Detection • Descriptive • Clustering • Association Rule Discovery • Sequential Pattern Discovery CISC 4631: Data Mining

24. Classification: Definition • Given a collection of records (training set) • Each record contains a set of attributes, one of the attributes is the class, which is to be predicted. • Find a model for class attribute as a function of the values of other attributes. • Model maps record to a class value • 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. • Can you think of examples of classification tasks? We will see several shortly. CISC 4631: Data Mining

25. Test Set Model Classification Example Task: Predict if someone cheats on their taxes categorical categorical continuous class Learn Classifier Training Set CISC 4631: Data Mining

26. 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 info as input attributes to learn a classifier model CISC 4631: Data Mining

27. Classification: Application 2 • Fraud Detection • Goal: Predict fraudulent cases in credit card transactions • Approach: • Use credit card transactions and info on account-holders as attributes • When and what does customer buy, how often 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. CISC 4631: Data Mining

28. 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. CISC 4631: Data Mining

29. 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 CISC 4631: Data Mining

30. Classifying Galaxies Courtesy: http://aps.umn.edu • Attributes: • Image features, • Characteristics of light waves received, etc. Early • Class: • Stages of Formation Intermediate Late • Data Size: • 72 million stars, 20 million galaxies • Object Catalog: 9 GB • Image Database: 150 GB CISC 4631: Data Mining

31. 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. CISC 4631: Data Mining

32. 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 one cluster are similar to one another • Data points in different clusters are not (less) similar • Similarity Measures: • Euclidean distance if attributes are continuous • Problem-specific measures • Can you think of any applications of clustering? CISC 4631: Data Mining

33. Illustrating Clustering • Euclidean Distance Based Clustering in 3-D space. Intracluster distances are minimized Intercluster distances are maximized CISC 4631: Data Mining

34. 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. CISC 4631: Data Mining

35. 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. CISC 4631: Data Mining

36. 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. Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} CISC 4631: Data Mining

37. Association Rule Discovery: Application 1 • Marketing and Sales Promotion: • Let the rule discovered be {Bagels, … } --> {Potato Chips} • Potato Chips as 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 antecedent and Potato chips in consequent=> Can be used to see what products should be sold with Bagels to promote sale of Potato chips! • Can help determine where to position store items CISC 4631: Data Mining

38. Association Rule Discovery: Application 2 • Supermarket shelf management • Goal: Identify items that are bought together by many customers • Approach: Process the point-of-sale data collected with barcode scanners to find item dependencies • A “classic” rule -- • If a customer buys diaper and milk, then he is very likely to buy beer. CISC 4631: Data Mining

39. (A B) (C) (D E) Sequential Pattern Discovery: Definition • 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 disovering patterns. Event occurrences in the patterns are governed by timing constraints. CISC 4631: Data Mining

40. 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) CISC 4631: Data Mining

41. Deviation/Anomaly Detection • Detect significant deviations from normal behavior • Applications: • Credit Card Fraud Detection • Network Intrusion Detection Typical network traffic at University level may reach over 100 million connections per day CISC 4631: Data Mining

42. Challenges of Data Mining • Scalability • Dimensionality • Complex and Heterogeneous Data • Data Quality • Data Ownership and Distribution • Privacy Preservation • Streaming Data CISC 4631: Data Mining

43. What is (and is not) Data Mining? • Based on the definitions of data mining, are these DM or not? • Finding a phone number in a directory • Not data mining (trivial) • Grouping related documents returned by search engine • Is data mining • Identifying who has a disease based on symptoms • Is data mining (not trivial) • Web search on keyword using search engine • May be data mining** ** More of an information retrieval task than data mining task, but since a search engine like Google does more than just keyword matching– it decides which web pages are important or not (a classification task that is part of DM) in order to get good results, the answer is not clear. CISC 4631: Data Mining

44. If you are Interested in Data Mining • Visit kdnuggets, an online newsletter and more • http://www.kdnuggets.com • You can arrange to have newsletter emailed to you • Also includes job openings • ACM SIGKDD is the professional organization associated with data mining • ACM Special Interest Group (SIG) on data mining • Can join SIGKDD for $22 or for $54 can also join ACM as student member • Conferences • KDD, ICDM, DMIN, … CISC 4631: Data Mining

45. Course Projects • Projects must involve data mining • May be research related • Examine some aspect of data mining • May be application oriented • Solve a realistic, complex, problem • May be a combination of both • Most problems involve some interesting aspect • In some cases can be a survey/analysis paper (i.e., just a report), but this will be atypical • Can be done individually or in teams of 2 • Ideally some projects can be published in a workshop or conference CISC 4631: Data Mining

46. Course Projects • Output • A written report, similar to a workshop or conference paper • Two example workshop papers from last time course offered : • http://storm.cis.fordham.edu/~gweiss/papers/ubdm05-mccarthy.pdf • http://storm.cis.fordham.edu/~gweiss/papers/ubdm05-ciraco.pdf • For more examples: • http://storm.cis.fordham.edu/~gweiss/publications.html and look at the various workshop/conference papers • A presentation in class near end of semester • Stretch goal: submit paper to a workshop or conference • I can help you CISC 4631: Data Mining

47. Course Projects • The sooner you start the better • Think about: • What you know about • What data you have access to • What type of problems you are interested in • Who you want to work with • I will provide some specific project ideas • Areas include: • Classification, clustering, association rules • Web and link mining, text mining, social network analysis CISC 4631: Data Mining