A model-based frequency constraint for mining associations from transaction data

Data Mining and Knowledge Discovery

Volumn 13, Issue 2, 2006, Pages 137-166

  Hahsler, Michael ^a  

^a VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

Author keywords

Associations; Data mining; Interest measures; Mixture models; Transaction data

Indexed keywords

FREQUENCY CONSTRAINT; MINING ALGORITHMS; MIXTURE MODELS; TRANSACTION DATA;

ALGORITHMS; DATA MINING; DATA TRANSFER; DATABASE SYSTEMS; KNOWLEDGE REPRESENTATION;

CONSTRAINT THEORY;

EID: 33748467598     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-005-0026-2     Document Type: Article

References (25)

1. Agarwal, R.C., Aggarwal, C.C., and Prasad, V.V.V. 2000. Depth first generation of long patterns. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 108-118.
2. Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 207-216.
3. Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, J. B. Bocca, M. Jarke, C. Zaniolo (Eds.) Santiago, Chile, pp. 487-499.
4. Borgelt, C. 2003. Efficient implementations of apriori and eclat. In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, B. Goethals, M.J. Zaki (Eds.) Melbourne, FL, USA.
5. 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, Tucson, Arizona, USA, pp. 255-264.
6. Creighton, C. and Hanash, S. 2003. Mining gene expression databases for association rules. Bioinformatics, 19(1):79-86.
7. Dempster, A.P., Laird, N.M., and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39:1-38.
8. DuMouchel, W. and Pregibon, D. 2001. Empirical bayes screening formultiitem associations. In Proceedings of the 7th ACM SIGKDD Intentional Conference on Knowledge Discovery in Databases and Data Mining, F. Provost, R. Srikant (Eds.) ACM Press, pp. 67-76.
9. Geyer-Schulz, A., Hahsler, M., and Jahn, M. 2002. A customer purchase incidence model applied to recommender systems. In WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, Lecture Notes in Computer Science LNAI 2356, R. Kohavi, B. Masand, M. Spiliopoulou, J. Srivastava (Eds.) Springer-Verlag, pp. 25-47.
10. Geyer-Schulz, A., Hahsler, M., Neumann, A., and Thede, A. 2003. Behaviorbased recommender systems as value-added services for scienti.c libraries. In Statistical Data Mining & Knowledge Discovery, H. Bozdogan, (Ed.) Chapman & Hall/CRC, pp. 433-454.
11. Han, J., Pei, J., Yin, Y., and Mao, R. 2004. Mining frequent patterns without candidate generation. Data Mining and Knowledge Discovery, 8:53-87.
12. Johnson, N.L., Kotz, S., and Kemp, A.W. 1993. Univariate Discrete Distributions, 2nd edn. New York: John Wiley & Sons.
13. Kohavi, R., Brodley, C., Frasca, B., Mason, L., and Zheng, Z. 2000. KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explorations, 2(2):86-98.
14. Kohavi, R. and Provost, F. 1988. Glossary of terms. Machine Learning, 30(2-3):271-274.
15. Liu, B., Hsu, W., and Ma, Y. 1999. Mining association rules with multiple minimum supports. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337-341.
16. Luo, J. and Bridges, S. 2000. Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection. International Journal of Intelligent Systems, 15(8):687-703.
17. Mannila, H., Toivonen, H., and Verkamo, A.I. 1994. Efficient algorithms for discovering association rules. In AAAI Workshop on Knowledge Discovery in Databases, U.M. Fayyad, R. Uthurasamy (Eds.) Seattle, Washington: AAAI Press, pp. 181-192.
18. Omiecinski, E.R. 2003. Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1):57-69.
19. Pei, J., Han, J., and Lakshmanan, L.V. 2001. Mining frequent itemsets with convertible constraints. In Proceedings of the 17th International Conference on Data Engineering, April 02-06, 2001, Heidelberg, Germany, pp. 433-442.
20. Provost, F. and Fawcett, T. 1997. Analysis and visualization of classi.er performance: Comparison under imprecise class and cost distributions. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Heckerman, D., Mannila, H., and Pregibon, D., editors, Newport Beach, CA: AAAI Press, pp. 43-48.
21. Seno, M. and Karypis, G. 2001. Lpminer: An algorithm for finding frequent itemsets using length decreasing support constraint. In Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November-2 December 2001, N. Cercone, T.Y. Lin, X. Wu (Eds.) San Jose, California, USA: IEEE Computer Society, pp. 505-512.
22. Silverstein, C., Brin, S., and Motwani, R. 1998. Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, 2:39-68.
23. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P.-N. 2000. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2): 12-23.
24. Xiong, H., Tan, P.-N., and Kumar, V. 2003. Mining strong affinity association patterns in data sets with skewed support distribution. In Proceedings of the IEEE International Conference on Data Mining, November 19-22, 2003, B. Goethals, M.J. Zaki (Eds.) Melbourne, Florida, pp. 387-394.
25. Zheng, Z., Kohavi, R., and Mason, L. 2001. Real world performance of association rule algorithms. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery in Databases and Data Mining, F. Provost, R. Srikant (Eds.) ACM Press, pp. 401-406.