1. Special Topics in Data Mining

2. 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. Special Topics in Data Mining Grade Structure Review Paper & Presentation: 30% Final Project Implementation & Present.: 40% Final Project Paper: 30%

4. Special Topics in Data Mining Data Mining in Specific field for Review 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

5. What is Data Mining? 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!)

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

7. What is Data Mining? “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

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. What is Data Mining? 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

10. What is Data Mining? 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:...

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

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 Data Data 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. 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, -, …, Relational Data 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 WavesTwo 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