Course Number:
CSc 80000 Section 5
Day/Time:
Mondays
Course Instructor:
Susan P. Imberman Ph.D.
Assistant Professor
Computer Science Department
College of Staten Island, CUNY
Voice Mail: 718-982-3273
Department Office: 718-982-982-2850
Email: imberman@mail.csi.cuny.edu
Home Page: www.cs.csi.cuny.edu/~imberman/
Course Page: www.cs.csi.cuny.edu/~imberman/DataMining
Course Description: This course will concentrate on reading papers that are of major significance to the area of Data Mining and Association Rule Algorithms. Each week students will be required to read between two and three papers, each of which will be discussed the following week. In addition, students will read and present papers that are related to the discussed papers. The goals of this course are to:
The papers for this course would constitute a significant core reading list for students wishing to satisfy the requirements of the first exam for a thesis topic in association rule algorithms.
Course References:
Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad, Gregory Piatestsky-Shaprio, Padhraic Smyth, and Ramasamy Uthurusamy Editors, AAAI Press / MIT Press ISBN 0-262-56097-6
Data Mining, Concepts and Techniques, Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers ISBN 1-55860-489-8
Essay: The Cognitive Style of PowerPoint: Pitching Out Corrupts Within, Edward R. Tufte , Graphics Press L.L.C.
Current papers in the field of association rule data mining will be discussed. These will be available online or by handout.
Course Organization: Course will be approximately 40% instructor lecture and 60% student presentation/discussion.
Grading Policy: Presentations 40%
Term Paper 40%
Class Participation (includes online discussion board) 20%
Course Outline: Note: this outline is subject to change.
Week: |
Topic |
1. |
Introduction to Data Mining and the KDD process |
2. |
Foundations of Data Mining |
3. |
Association Rule Mining - Apriori Algorithm |
4. |
Other Association Rule Algorithms (FP-growth, DCH, Partition Update, DIC) |
5. |
Maximal Frequent Itemsets, Closed Frequent Itemsets |
6. |
Incremental Association Rules |
7. |
Temporal Association Rules |
8. |
Quantitative Association Rules |
9. |
Interestingness |
10. |
Efficient Association Rule Algorithms (FIMI workshop) |
11. |
Beyond Market Baskets |
12. |
Data Mining and Society - Ethical Issues |
13. |
Association Rule/Data Mining Applications |
14. |
Association Rule/Data Mining Applications |
15. |
Term papers due |