| 1998 Fall | EE 380L Data Mining | Unique # 15250 |
COURSE TOPICS
The following outline describes the list and order of topics to be covered (given enough time), coupled with the list of primary references. These primary references will be available to all students via HKN. A secondary list of papers is available from the course home page.
Student presentations (15-20 minutes) will be interwoven with the class lectures. Students are strongly urged to select a paper from the primary or secondary list for presentation. You can select a paper outside these lists too, but you have to convince me first that the selected paper is a good one and relevant to the course. If you select a paper on Topic ``A'', your presentation will come right after I finish covering that topic.
1. Introduction and Overview
2. Data Warehousing and OLAP: A quick overview of these related areas and some basic functionalities that they provide [4,5]
3. Data Reduction, Feature Selection/Extraction and Clustering
4. Association Rules
FAY 12, [10]
5. Graphical Models for Discovering Knowledge Bayesian Belief Networks, Hidden Markov Models, Probabilistic Inference Networks.
6. Classification and Prediction
7. Mining of sequential patterns and time series
8. Deviation or exception detection [20]
9. Computational Aspects: Algorithms from previous topics, but with emphasis on computational efficiency [21]
10. Term Paper Presentations
11. Course wrap-up and Philosophy