Data Mining Topics and Techniques
Catalog Description: This course will focus on the development of relevant tools, methods, and design of Knowledge from Data (KDD) Systems. Further, it will concentrate on the design and implementation of an advanced data mining system with expectations of optimal performance and flexibility.
Total Credits: 3
Course Coordinator: Currently unassigned
URL: None available.
Prereq: Graduate Standing or Permission
Textbook: Jawai Han and Micheline Kamber, Data Mining: Concepts and Techniques, 3rd Ed., Morgan Kaufmann, 20011, ISBN-10: 0123814790 ISBN-13: 978-0123814791.
Note: This course is not scheduled to be taught again in the near future. This posting is for information.
Syllabus: CS 526 Syllabus (PDF)
Major Topics Covered
- Data preprocessing and reduction
- Data warehouses and data mining
- Mining frequent patterns
- Associations, correlation, classification and prediction
- Cluster analysis
- Mining data streams, time series, and sequences
- Bayesian classification
- Decision tree induction
- Back propagation
- Vector machines, hardware support
- Some aspects of fuzzy logic and neural networks