Download Homework 7

CIS526: Homework 7
Assigned: Nov 19, 2004
Due: in class, Tue, Nov 30, 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.
Ali K, Stam WV, Tivo: Making Show Recommendations Using a Distributed Collaborative Filtering
Architecture, Proceedings ACM SIGKDD International Conference on Knowledge discovery and Data Mining,
394 – 401, Seattle, 2004.
Donoho S, Early Detection of Insider Trading in Option Markets, Proceedings ACM SIGKDD International
Conference on Knowledge discovery and Data Mining, 420 – 429, Seattle, 2004.
Yoshida K, et al., Density-Based Spam Detector, Proceedings ACM SIGKDD International Conference on
Knowledge discovery and Data Mining, 486 – 493, Seattle, 2004.
Han J, Pei J, Dong G, Wang K, Efficient Computation of Iceberg Cubes With Complex Measures, Proc. 2001
ACM SIGMOD International Conf. on Management of Data, Santa Barbara, 2001.
Keogh E, Pazzani M. An Enhanced Representation of Time Series which Allows Fast and Accurate
Classification, Clustering and Relevance Feedback, Proceedings of the 4th International Conference on
Knowledge Discovery and Data Mining, New York, 239-241, 1998.
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 30, 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!!