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).
- Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques,
Morgan Kaufmann, 1998.
- U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (Eds.), Advances in Knowledge
Discovery and Data Mining, AAAI/MIT Press, 1996.
- Communications of the ACM, Special Issue on Data Mining, November 1996.
- Current papers from journals and magazines, provided during the course.
- What is Data Mining?
- Mining from Different Databases.
- Classification of Data Mining Techniques
- Data Warehousing: General Principles, Modeling, Design,
Implementation, and Optimization.
- On-Line Analytical Processing
- Data Mining Primitives, Languages, and Interfaces
- Concept Hierarchies, Description
- Statistical Perspectives on Data Mining
- Classification and Clustering
- Time-Series Analysis
- Deviation Detection
- Sequential Patterns
- Associations and Rule Generation
- Genetic Algorithms
- Incremental Mining
- Scalability issues of Data Mining Algorithms
- Visualization of Data Mining Results
- High Performance Computing Applications in Data Mining
- Case Studies
- Mining the Web (document classification, adaptive documents)
- Predicting Equity Returns from Securities Data
- Decision Support Systems