CS 590D Course Outline

Course Outline:
  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.

Course Syllabus:
  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

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