Download Homework 6

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.
2.
3.
4.
5.
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
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.
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.
GM. Weiss, F. Provost, Learning When Training Data are Costly: The Effect of Class Distribution on
Tree Induction, Volume 19, 315-354, 2003.
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!!