3460:676 Data Mining

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3460:676 Data Mining

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Study fundamental data mining algorithms and their applications in the ... Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice.

Course Number: Course Name: Course Credits: Schedule:

3460:676 Data Mining 3.0

Syllabus Date: Prepared By:

Fall, 2004 C.-C. Chan

Prerequisites:

3460:475/575 or permission of instructor

Text: Han, Jiawei and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001. ISBN: 1-55860-489-8

Bulletin Description: Study fundamental data mining algorithms and their applications in the process of Knowledge Discovery from Databases. Study data warehousing systems and architectures. Detailed Description: Course Goals: 1. 2. 3. 4.

Study the process of Knowledge Discovery from Databases. Study fundamental data mining algorithms such as association rules, classification, and clustering algorithms. Study data cube and OLAP. Study data integration architectures: federated database, data warehouse, and mediated-based systems.

Topics: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Overview of KDD and Data Mining Data Warehouse and OLAP Systems Data Cubes. MS SQL Server. Issues and Techniques in Data Preprocessing Mining Classification Rules Mining Association Rules in Large Databases Cluster Analysis Mining sequential patterns Data Integration

Computer Usage: There will be team programming projects. Programs will be developed and run on PC or Linux workstations. References:  Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall, 2003, 0-13-088892-3.

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Garcia-Molina H. et. al., Database Systems the Complete Book, Prentice Hall 2002, 0-13-031995-3, Chapter 20. Witten, I.H., E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation, Morgan Kaufmann Publishers, 2000, 155860-552-5. Seidman, Claude, Data Mining with Microsoft SQL Server 2000 Technical Reference, May 1, 2001, Microsoft Press, 0-73-561271-4. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996. Ryszard S. Michalski, Ivan Bratko, Miroslav Kubat, eds., MACHINE LEARNING & DATA MINING: Methods and Applications, John Wiley, 1998. Tom Mitchell, Machine Learning, McGraw Hill, 1997. David J. Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining , MIT Press, 2000. Gray, J., S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh, “Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals,” Data Mining and Knowledge Discovery, 1, 29-53, Kluwer Academic Publishers, (1997). www.kdnuggets.com