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Special Topics in Data Mining

Special Topics in Data Mining. Special Topics in Data Mining. Direct Objectives To learn data mining techniques To see their use in real-world/research applications To get an understanding of the methodological principles behind data mining

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Special Topics in Data Mining

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  1. SpecialTopics in Data Mining

  2. SpecialTopics in Data Mining Direct Objectives • To learn data mining techniques • To see their use in real-world/research applications • To get an understanding of the methodological principles behind data mining • To be able to read about data mining in the popular press with a critical eye • To implement & use data mining models using DM software

  3. SpecialTopics in Data Mining GradeStructure Review Paper & Presentation : 30% Final Project Implementation & Present. :40% Final Project Paper : 30%

  4. Data Mining in Specific fieldforReview Paper • Data Mining in Security • Data Mining in Telecommunications and Control • Text and Web Mining • Data Mining in Biomedicine and Science • Data Mining for Insurance • Data Mining in Banking and Commercial • Data Mining in Sales Marketing and Finance • Data Mining in Business SpecialTopics in Data Mining

  5. Not well defined…. Since Data Mining is Confluence of Multiple Disciplines No one can agree on what data mining is! In fact the experts have very different descriptions: Different fields have different views of what data mining is (also different terminology!) What is Data Mining?

  6. Database Technology Statistics Data Mining Machine Learning Visualization Information Science Other Disciplines Since Data Mining is Confluence of Multiple Disciplines What is Data Mining?

  7. “finding interesting structure (patterns, statistical models, relationships) in data bases”. - Fayyad, Chaduriand • “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” - Fayyad What is Data Mining?

  8. What is Data Mining? • “a knowledge discovery process of extracting previously unknown, actionable information from very large data bases” – Zorne • “a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions.”--- Edelstein

  9. Data mining is the process of extracting hidden patterns from data. • Data mining is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management. • “data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” Hand, Mannila, Smyth What is Data Mining?

  10. Knowledge Discovery in Databases (KDD) • Data Mining, also popularly known as Knowledge Discovery in Databases (KDD)... • The Knowledge Discovery in Databases process comprises of a few steps leading from raw data collections to some form of new knowledge. The iterative process consists of the following steps: (From Zaiane) • Data cleaning: ... • Data integration: ... • Data selection: ... • Data transformation: ... • Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful. • Pattern evaluation: ... • Knowledge representation: ... What is Data Mining?

  11. Knowledge Discovery in Databases (KDD) • ….. • Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful. • ….. What is Data Mining?

  12. What is Data Mining? • Software • Can use any software you like – must know how to input, manipulate, graph, and analyze data. • SAS, Weka, SPSS, Systat, Enterprise Miner, JMP, Minitab, Matlab, SQL Server

  13. What is Data Mining? • Software • Can use any software you like – must know how to input, manipulate, graph, and analyze data. • SAS, Weka, SPSS, Systat, Enterprise Miner, JMP, Minitab, Matlab, SQL Server

  14. Data DataData • It’s all about the data - where does it come from? • www • Gene • Business processes/transactions • Telecommunications and networking • Medical imagery • Government, demographics (data.gov!) • Sensor networks • sports

  15. What is Data? • Collection of objects and their attributes • An attribute is a property or characteristic of an object • Examples: eye color of a person, temperature, etc. • Attribute is also known as variable, field, characteristic, or feature • A collection of attributes describe an object • Object is also known as record, point, case, sample, entity, or instance • Attribute values are numbers or symbols assigned to an attribute Attributes Objects

  16. Record Data • Data that consists of a collection of records, each of which consists of a fixed set of attributes

  17. Document Data • Each document becomes a `term' vector, • each term is a component (attribute) of the vector, • the value of each component is the number of times the corresponding term occurs in the document.

  18. Transaction Data • A special type of record data, where • each record (transaction) involves a set of items. • For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.

  19. Transaction Data weblogs, phone calls… 128.195.36.195, -, 3/22/00, 10:35:11, W3SVC, SRVR1, 128.200.39.181, 781, 363, 875, 200, 0, GET, /top.html, -, 128.195.36.195, -, 3/22/00, 10:35:16, W3SVC, SRVR1, 128.200.39.181, 5288, 524, 414, 200, 0, POST, /spt/main.html, -, 128.195.36.195, -, 3/22/00, 10:35:17, W3SVC, SRVR1, 128.200.39.181, 30, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.195.36.101, -, 3/22/00, 16:18:50, W3SVC, SRVR1, 128.200.39.181, 60, 425, 72, 304, 0, GET, /top.html, -, 128.195.36.101, -, 3/22/00, 16:18:58, W3SVC, SRVR1, 128.200.39.181, 8322, 527, 414, 200, 0, POST, /spt/main.html, -, 128.195.36.101, -, 3/22/00, 16:18:59, W3SVC, SRVR1, 128.200.39.181, 0, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:54:37, W3SVC, SRVR1, 128.200.39.181, 140, 199, 875, 200, 0, GET, /top.html, -, 128.200.39.17, -, 3/22/00, 20:54:55, W3SVC, SRVR1, 128.200.39.181, 17766, 365, 414, 200, 0, POST, /spt/main.html, -, 128.200.39.17, -, 3/22/00, 20:54:55, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:55:07, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 1061, 382, 414, 200, 0, POST, /spt/main.html, -, 128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:55:39, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:56:03, W3SVC, SRVR1, 128.200.39.181, 1081, 382, 414, 200, 0, POST, /spt/main.html, -, 128.200.39.17, -, 3/22/00, 20:56:04, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:56:33, W3SVC, SRVR1, 128.200.39.181, 0, 262, 72, 304, 0, GET, /top.html, -, 128.200.39.17, -, 3/22/00, 20:56:52, W3SVC, SRVR1, 128.200.39.181, 19598, 382, 414, 200, 0, POST, /spt/main.html, -,

  20. Graph Data • Examples: Generic graph and HTML Links

  21. Ordered Data • Genomic sequence data

  22. Time Series Data

  23. Spatio-Temporal Data Average Monthly Temperature of land and ocean

  24. Relational Data 128.200.39.17, -, 3/22/00, 20:55:07, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 1061, 382, 414, 200, 0, POST, /spt/main.html, -, 128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -, 128.195.36.195, -, 3/22/00, 10:35:11, W3SVC, SRVR1, 128.200.39.181, 781, 363, 875, 200, 0, GET, /top.html, -, 128.195.36.195, -, 3/22/00, 10:35:16, W3SVC, SRVR1, 128.200.39.181, 5288, 524, 414, 200, 0, POST, /spt/main.html, -, 128.195.36.195, -, 3/22/00, 10:35:17, W3SVC, SRVR1, 128.200.39.181, 30, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -, …, 128.195.36.195, Doe, John, 12 Main St, 973-462-3421, Madison, NJ, 07932 114.12.12.25,Trank, Jill, 11 Elm St, 998-555-5675, Chester, NJ, 07911 … 07911, Chester, NJ, 07954, 34000, , 40.65, -74.12 07932, Madison, NJ, 56000, 40.642, -74.132 … • Most large data sets are stored in relational data sets • Special data query language: SQL • Oracle, MSFT, IBM • Good open source versions: MySQL, PostGres

  25. Data Quality • What kinds of data quality problems? • How can we detect problems with the data? • What can we do about these problems? • Examples of data quality problems: • Noise and outliers • missing values • duplicate data

  26. Noise • Noise refers to modification of original values • Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen Two Sine Waves Two Sine Waves + Noise

  27. Outliers • Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set

  28. Missing Values • Reasons for missing values • Information is not collected (e.g., people decline to give their age and weight) • Attributes may not be applicable to all cases (e.g., annual income is not applicable to children) • Handling missing values • Eliminate Data Objects • Estimate Missing Values • Ignore the Missing Value During Analysis • Replace with all possible values (weighted by their probabilities)

  29. Duplicate Data • Data set may include data objects that are duplicates, or almost duplicates of one another • Major issue when merging data from heterogeous sources • Examples: • Same person with multiple email addresses • Data cleaning • Process of dealing with duplicate data issues

  30. Examples of Data Mining Successes • Market Basket (WalMart) • Recommender Systems (Amazon.com) • Fraud Detection in Telecommunications (AT&T) • Target Marketing / CRM • Financial Markets • DNA Microarray analysis • Web Traffic / Blog analysis

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