Ryszard S. Michalski
This course covers principles, methods, and practical tools for deriving understandable and actionable knowledge from databases, and other information sources. Due to an explosive growth of databases in sciences and other domains of human activity, an automated derivation of useful knowledge from data represents one of the most important directions of computational sciences and information technology. Students will learn through lectures, studying assigned or self-selected reading, making presentations, and conducting projects in the areas of their interest.
1. Motivation and goals
2. Fundamental concepts
3. Issues in knowledge discovery
4. Databases and warehouses
5. Data selection and preparation
6. Statistics-based methods
7. Machine learning-based and other methods
8. Integrated data mining systems
9. Frontier research and future directions
R. S. Michalski, “Lecture Notes on Data Mining and Knowledge Discovery,” GMU, Fall 2001.
J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.