Detecting group differences: Mining contrast sets

Data Mining and Knowledge Discovery

Volumn 5, Issue 3, 2001, Pages 213-246

  Bay, Stephen D ^a   Pazzani, Michael J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Association rules; Change detection; Contrast sets; Data mining

Indexed keywords

EID: 23044527560     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1011429418057     Document Type: Article

References (24)

1. Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining associations between sets of items in massive database. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207-216.
2. Agrawal, R., Psaila, G., Wimmers, E., and Zait, M. 1995. Querying shapes of histories. In Proceedings of the 21st International Conference on Very Large Databases.
3. Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases.
4. Agresti, A. 1990. Categorical Data Analysis. New York: John Wiley & Sons.
5. Bay, S.D. 1999. The UCIKDD archive, [http://kdd.ics.uci.edu/]. Irvine, CA: University of California, Department of Information and Computer Science.
6. Bay, S.D. and Pazzani, M.J. 1999. Detecting change in categorical data: Mining contrast sets. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 302-306.
7. Bayardo, R.J. 1998. Efficiently mining long patterns from databases. In Proceedings of the ACM SIGMOD Conference on Management of Data.
8. Bayardo, R.J., Agrawal, R., and Gunopulos, D. 1999. Constraint-based rule mining in large, dense databases. In Proceedings 15th International Conference on Data Engineering.
9. Bazaraa, M.S. and Shetty, C.M. 1979. Nonlinear Programming: Theory and Algorithms. New York: John Wiley & Sons.
10. Bishop, Y.M.M., Fienberg, S.E., and Holland, P.W. 1975. Discrete Multivariate Analysis: Theory and Practice: The MIT Press, Cambridge, Massachusetts.
11. Blake, C. and Merz, C.J. 1998. UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. [http://www.ics.uci.edu/̃mlearn/MLRepository.html].
12. Brin, S., Motwani, R., Ullman, J.D., and Tsur, S. 1997. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 255-264.
13. Chakrabarti, S., Sarawagi, S., and Dom, B. 1998. Mining surprising patterns using temporal description length. In Proceedings of the 24th International Conference on Very Large Databases.
14. Cohen, J. 1990. Things I have learned (so far). American Psychologist, 45:1304-1312.
15. Darity, W.A. 1998. Intergroup disparity: Economic theory and social science evidence. Southern Economic Journal, 64:805-826.
16. Davies, J. and Billman, D. 1996. Hierarchical categorization and the effects of contrast inconsistency in an unsupervised learning task. In Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, p. 750.
17. Dong, G. and Li, J. 1999. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
18. Everitt, B.S. 1992. The Analysis of Contingency Tables, 2nd ed. Chapman and Hall, London, U.K.
19. Ganti, V., Gehrke, J.E., Ramakrishnan, R., and Loh, W. 1999. A framework for measuring changes in data characteristics. In Proceedings of Eighteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems.
20. Glenn, N.D. 1977. Cohort Analysis. Newbury Park, CA: Sage Publications.
21. Hochberg, Y. and Tamhane, A.C. 1987. Multiple Comparison Procedures. New York: John Wiley & Sons.
22. Hoschka, P. and Klösgen, W. 1991. A support system for interpreting statistical data. In Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley (Eds.). AAAI Press, Menlo Park, CA, pp. 325-346.
23. Keogh, E. and Pazzani, M.J. 1998. An enhanced representation of time series that allows fast and accuracte classification, clustering, and relevance feedback. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining.
24. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkamo, A.I. 1994. Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management, pp. 401-407.