Download 3460:676 Data Mining

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:
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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:
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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).
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