CS 590D Course Outline

• Introduction to Data Mining and Knowledge Discovery
• Background/Review of Databases, Algorithms and Data Mining tools
• Advanced Topics: Parallel Data Mining, Integrated Discovery Systems and other issues
• Case Studies from actual projects
• Medium Sized Term Project of student's choice (Students are allowed to work in groups of 2-3 depending on the magnitude of the project).

1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 1998.
2. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
3. Communications of the ACM, Special Issue on Data Mining, November 1996.
4. Current papers from journals and magazines, provided during the course.

1. What is Data Mining?
2. Mining from Different Databases.
3. Classification of Data Mining Techniques
4. Data Warehousing: General Principles, Modeling, Design, Implementation, and Optimization.
5. On-Line Analytical Processing
6. Data Mining Primitives, Languages, and Interfaces
7. Concept Hierarchies, Description
8. Statistical Perspectives on Data Mining
9. Classification and Clustering
10. Time-Series Analysis
11. Deviation Detection
12. Sequential Patterns
13. Associations and Rule Generation
14. Genetic Algorithms
15. Incremental Mining
16. Scalability issues of Data Mining Algorithms
17. Visualization of Data Mining Results
18. High Performance Computing Applications in Data Mining
19. Case Studies
    • Mining the Web (document classification, adaptive documents)
    • Predicting Equity Returns from Securities Data
    • Decision Support Systems