Readings In Data Mining - Association Rule Mining


Course Number:

CSc 80000 Section 5



Mondays 9:30 -11:30


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


Home Page:

Course Page:


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:

  1. Survey the field of data mining, with a focus on association rule mining.
  2. See the progression of a research topic, from the seminal paper to improvements and new work based on the foundation work.
  3. Learn how to critically read research papers.
  4. Learn the proper format for writing good research papers with the intent of writing a research or survey paper.
  5. Learn how to present a paper in a clear, concise, and understandable (to the audience) method.

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.





Introduction to Data Mining and the KDD process


Foundations of Data Mining


Association Rule Mining - Apriori Algorithm


Other Association Rule Algorithms (FP-growth, DCH, Partition Update, DIC)


Maximal Frequent Itemsets, Closed Frequent Itemsets


Incremental Association Rules


Temporal Association Rules


Quantitative Association Rules




Efficient Association Rule Algorithms (FIMI workshop)


Beyond Market Baskets


Data Mining and Society - Ethical Issues


Association Rule/Data Mining Applications


Association Rule/Data Mining Applications


Term papers due