CIS526: Homework 6

Assigned: Nov 09, 2004

Due: in class, Tue, Nov 16, 2004

Homework Policy

·  All assignments are INDIVIDUAL! You may discuss the problems with your colleagues, but you must solve the homework by yourself. Please acknowledge all sources you use in the homework (papers, code or ideas from someone else).

Assignments should be submitted in class on the day when they are due. No credit is given for assignments submitted at a later time, unless you have a medical problem.

Problems

Write two half-page reports (font 11pt, 1-inch margin) describing motivation, methodology, experimental results for two of the following 5 papers

1.  G. Das, K.-I. Lin, H. Mannila, G. Renganathan, P. Smyth. Rule discovery from time series. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, 1998

2.  M. Steinbach, PN. Tan, V. Kumar, S. Klooster, C. Potter, Data Mining for the Discovery of Ocean Climate Indices, Proc of the Fifth Workshop on Scientific Data Mining at 2nd SIAM International Conference on Data Mining, 2002.

3.  GH. John, P. Langley. Static versus dynamic sampling for data mining. In E. Simoudis, J-W. Han, and U. Fayyad, editors, Proceedings, Second International Conference on Knowledge Discovery and Data Mining, pages 367--370, Menlo Park, CA, 1996. AAAI Press.

4.  GM. Weiss, F. Provost, Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction, Volume 19, 315-354, 2003.

5.  JL. Herlocker, JA. Konstan, A. Borchers, J. Riedl, An Algorithmic Framework for Performing Collaborative Filtering. SIGIR Conference, 230-237, 1999.

You are free to select any two of the five offered papers. Please note that all 5 papers will be presented in class on Nov 16, 2004. You can download papers from the course web site. Within the page limit, please also discuss what do you consider as strongest or most important parts of the paper and what are the drawbacks or problems with the paper. The paper is a very nice example of high-quality empirical work in data mining. Pay special attention to the style of presentation, since you will be expected to be able to emulate it in your course project report.

Good Luck!!