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DATA MINING Part I IIIT Allahabad

DATA MINING Part I IIIT Allahabad. Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Dallas, Texas 75275, USA mhd@lyle.smu.edu http://lyle.smu.edu/~ mhd/dmiiit.html

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DATA MINING Part I IIIT Allahabad

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  1. DATA MINING Part IIIIT Allahabad Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Dallas, Texas 75275, USA mhd@lyle.smu.edu http://lyle.smu.edu/~mhd/dmiiit.html Some slides extracted from Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002. Support provided by Fulbright Grant and IIIT Allahabad

  2. Data Mining Outline Part I: Introduction (19/1 – 20/1) Part II: Classification (24/1 – 27/1) Part III: Clustering (31/1 – 3/2) Part IV: Association Rules (7/2 – 10/2) Part V: Applications (14/2 – 17/2)

  3. Class Structure Each class is two hours Tuesday/Wednesday presentation Thursday/Friday Lab

  4. Data Mining Part I Introduction Outline Goal: Provide an overview of data mining. • Lecture • Define data mining • Data mining vs. databases • Basic data mining tasks • Data mining development • Data mining issues • Lab • Download XLMiner and Weka • Analyze simple dataset

  5. Introduction • Data is growing at a phenomenal rate • Users expect more sophisticated information • How? • UNCOVER HIDDEN INFORMATION • DATA MINING

  6. Data Mining Definition • Finding hidden information in a database • Fit data to a model • Similar terms • Exploratory data analysis • Data driven discovery • Deductive learning

  7. Data Mining Algorithm • Objective: Fit Data to a Model • Descriptive • Predictive • Preference – Technique to choose the best model • Search – Technique to search the data • “Query”

  8. Query Well defined SQL Query Poorly defined No precise query language Database Processing vs. Data Mining Processing • Data • Operational data • Data • Not operational data • Output • Precise • Subset of database • Output • Fuzzy • Not a subset of database

  9. Query Examples • Database • Data Mining • Find all credit applicants with last name of Smith. • Identify customers who have purchased more than $10,000 in the last month. • Find all customers who have purchased milk • Find all credit applicants who are poor credit risks. (classification) • Identify customers with similar buying habits. (Clustering) • Find all items which are frequently purchased with milk. (association rules)

  10. Basic Data Mining Tasks • Classification maps data into predefined groups or classes • Supervised learning • Prediction • Regression • Clustering groups similar data together into clusters. • Unsupervised learning • Segmentation • Partitioning

  11. Basic Data Mining Tasks (cont’d) • Link Analysis uncovers relationships among data. • Affinity Analysis • Association Rules • Sequential Analysis determines sequential patterns.

  12. CLASSIFICATION Assign data into predefined groups or classes.

  13. But it isn’t Magic Suppose you knew that a specific cave had gold: • What would you look for? • How would you look for it? • Might need an expert miner You must know what you are looking for You must know how to look for you

  14. Description Behavior Associations “If it looks like a duck, walks like a duck, and quacks like a duck, then it’s a duck.” “If it looks like a terrorist, walks like a terrorist, and quacks like a terrorist, then it’s a terrorist.” Classification Clustering Link Analysis (Profiling) (Similarity)

  15. x <90 >=90 x A <80 >=80 x B <70 >=70 x C <50 >=60 D F Classification Ex: Grading

  16. Katydids Given a collection of annotated data. (in this case 5 instances of Katydids and five of Grasshoppers), decide what type of insect the unlabeled example is. Grasshoppers (c) Eamonn Keogh, eamonn@cs.ucr.edu

  17. The classification problem can now be expressed as: • Given a training database predict the class label of a previously unseen instance previously unseen instance = (c) Eamonn Keogh, eamonn@cs.ucr.edu

  18. 10 9 8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 9 10 AntennaLength Abdomen Length Katydids (c) Eamonn Keogh, eamonn@cs.ucr.edu Grasshoppers

  19. Facial Recognition (c) Eamonn Keogh, eamonn@cs.ucr.edu

  20. 1 0.5 0 50 100 150 200 250 300 350 400 450 0 Handwriting Recognition (c) Eamonn Keogh, eamonn@cs.ucr.edu George Washington Manuscript

  21. Anomaly Detection

  22. CLUSTERING Partition data into previously undefined groups.

  23. http://149.170.199.144/multivar/ca.htm

  24. What is Similarity? (c) Eamonn Keogh, eamonn@cs.ucr.edu

  25. Two Types of Clustering Partitional Hierarchical (c) Eamonn Keogh, eamonn@cs.ucr.edu

  26. Hierarchical Clustering ExampleIris Data Set Versicolor Setosa Virginica The data originally appeared in Fisher, R. A. (1936). "The Use of Multiple Measurements in Axonomic Problems," Annals of Eugenics 7, 179-188. Hierarchical Clustering Explorer Version 3.0, Human-Computer Interaction Lab, University of Maryland, http://www.cs.umd.edu/hcil/multi-cluster .

  27. http://www.time.com/time/magazine/article/0,9171,1541283,00.htmlhttp://www.time.com/time/magazine/article/0,9171,1541283,00.html

  28. Microarray Data Analysis Each probe location associated with gene Color indicates degree of gene expression Compare different samples (normal/disease) Track same sample over time Questions • Which genes are related to this disease? • Which genes behave in a similar manner? • What is the function of a gene? Clustering • Hierarchical • K-means

  29. Microarray Data - Clustering "Gene expression profiling identifies clinically relevant subtypes of prostate cancer" Proc. Natl. Acad. Sci. USA, Vol. 101, Issue 3, 811-816, January 20, 2004

  30. ASSOCIATION RULES/ LINK ANALYSIS Find relationships between data

  31. ASSOCIATION RULES EXAMPLES People who buy diapers also buy beer If gene A is highly expressed in this disease then gene A is also expressed Relationships between people Book Stores Department Stores Advertising Product Placement

  32. Data Mining Introductory and Advanced Topics, by Margaret H. Dunham, Prentice Hall, 2003. DILBERT reprinted by permission of United Feature Syndicate, Inc.

  33. Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.

  34. No/Little Cheating Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.

  35. Rampant Cheating Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.

  36. Jialun Qin, Jennifer J. Xu, DaningHu, Marc Sageman and Hsinchun Chen, “Analyzing Terrorist Networks: A Case Study of the Global Salafi Jihad Network”  Lecture Notes in Computer Science, Publisher: Springer-Verlag GmbH, Volume 3495 / 2005 , p. 287.

  37. Ex: Stock Market Analysis • Example: Stock Market • Predict future values • Determine similar patterns over time • Classify behavior

  38. Ex: Stock Market Analysis

  39. Data Mining vs. KDD • Knowledge Discovery in Databases (KDD): process of finding useful information and patterns in data. • Data Mining: Use of algorithms to extract the information and patterns derived by the KDD process.

  40. KDD Process • Selection: Obtain data from various sources. • Preprocessing: Cleanse data. • Transformation: Convert to common format. Transform to new format. • Data Mining: Obtain desired results. • Interpretation/Evaluation: Present results to user in meaningful manner. Modified from [FPSS96C]

  41. KDD Process Ex: Web Log • Selection: • Select log data (dates and locations) to use • Preprocessing: • Remove identifying URLs; Remove error logs • Transformation: • Sessionize (sort and group) • Data Mining: • Identify and count patterns; Construct data structure • Interpretation/Evaluation: • Identify and display frequently accessed sequences. • Potential User Applications: • Cache prediction • Personalization

  42. Related Topics Databases OLTP OLAP Information Retrieval

  43. DB & OLTP Systems • Schema • (ID,Name,Address,Salary,JobNo) • Data Model • ER • Relational • Transaction • Query: SELECT Name FROM T WHERE Salary > 100000 DM: Only imprecise queries

  44. Classification/Prediction is Fuzzy Loan Amnt Reject Reject Accept Accept Simple Fuzzy

  45. Information Retrieval • Information Retrieval (IR): retrieving desired information from textual data. • Library Science • Digital Libraries • Web Search Engines • Traditionally keyword based • Sample query: Find all documents about “data mining”. DM: Similarity measures; Mine text/Web data.

  46. Information Retrieval (cont’d) • Similarity: measure of how close a query is to a document. • Documents which are “close enough” are retrieved. • Metrics: • Precision = |Relevant and Retrieved| |Retrieved| • Recall= |Relevant and Retrieved| |Relevant|

  47. IR Query Result Measures and Classification IR Classification

  48. OLAP • Online Analytic Processing (OLAP): provides more complex queries than OLTP. • OnLine Transaction Processing (OLTP): traditional database/transaction processing. • Dimensional data; cube view • Visualization of operations: • Slice: examine sub-cube. • Dice: rotate cube to look at another dimension. • Roll Up/Drill Down DM: May use OLAP queries.

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