Building an association rules framework to improve product assortment decisions

Data Mining and Knowledge Discovery

Volumn 8, Issue 1, 2004, Pages 7-23

  Brijs, Tom ^a   Swinnen, Gilbert ^a   Vanhoof, Koen ^a   Wets, Geert ^a  

^a HASSELT UNIVERSITY   (Belgium)

Author keywords

Association rules; Frequent itemset; Product assortment decisions

Indexed keywords

CUSTOMER SATISFACTION; DATA ACQUISITION; DECISION MAKING; LAWS AND LEGISLATION; PRODUCT DEVELOPMENT; SALES; SENSITIVITY ANALYSIS;

ASSOCIATION RULES; CUSTOMER ORIENTATION; FREQUENT ITEMSETS; PRODUCT ASSORTMENT DECISIONS;

INDUSTRIAL ECONOMICS;

EID: 3543086797     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:DAMI.0000005256.79013.69     Document Type: Article

References (33)

1. Adomavicius, G. and Tuzhilin, A. 1997. Discovery of actionable patterns in databases: The action hierarchy approach. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97), pp. 111-114.
2. Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD Conference on Management of Data, pp. 207-216.
3. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. 1996. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, AAAI Press, pp. 307-328.
4. Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases, pp. 487-499.
5. Ahmed, K.H., El-Makky, N.M., and Taha, Y. 2000. A note on "Beyond market baskets: Generalizing association rules to correlations." SIGKDD Explorations, 1(2):46-48.
6. Ali, K., Manganaris, S., and Srikant, R. 1997. Partial classification using association rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 115-118.
7. Anand, S., Hughes, J., Bell, D., and Patrick, A. 1997. Tackling the cross-sales problem using data mining. In Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 331-343.
8. Bixby, R.E., Fenelon, M., Gu, Z., Rothberg, E., and Wunderling, R. 1999. MIP: Theory and practice - Closing the gap. ILOG CPLEX Technical report.
9. Blischok, T. 1995. Every transaction tells a story. Chain Store Age Executive with Shopping Center Age, 71(3):5057.
10. Böcker, F. 1978. Die Bestimmung der Kaufverbundenheit von Produkten. In Schriften zum Marketing, band 7.
11. Brijs, T., Vanhoof, K., and Wets, G. 2000. Reducing redundancy in characteristic rule discovery by using integer-programming techniques. Accepted for Publication in the Intelligent Data Analysis Journal.
12. Brin, S., Motwani, R., Ullman, J., and Tsur, S. 1997. Dynamic itemset counting and implication rules for market basket data. In Proceedings ACM SIGMOD International Conference on Management of Data, pp. 255-264.
13. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. 1997. Discovering Data Mining: From Concept to Implementation. NJ: Prentice Hall.
14. Guillaume, S., Guillet, F., and Philippé, J. 1998. Improving the discovery of association rules with intensity of implication. In Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery, pp. 318-327.
15. Hedberg, S. 1995. The data gold rush. BYTE, pp. 83-88.
16. Hruschka, H., Lukanowicz, M., and Buchta, C. 1991. Cross-category sales promotion effects. Journal of Retailing and Consumer Services, 6(2):99-106.
17. Kendall, M. and Stuart, A. 1979. The Advanced Theory of Statistics: Inference and Relationship. London: Charles Griffin and Company Ltd.
18. Kleinberg, J., Papadimitriou, C., and Raghavan, P. 1998. A microeconomic view of data mining. Data Mining and Knowledge Discovery Journal, 2(4):311-324.
19. 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.
20. Liu, B. and Hsu, W. 1996. Post-analysis of learned rules. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 828-834.
21. Liu, B., Hsu, W., and Ma, Y. 1999. Pruning and summarizing the discovered associations. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 125-134.
22. Mannila, H. 1997. Methods and problems in data mining. In Proceedings of the International Conference on Database Theory, pp. 41-55.
23. Merkle, E. 1981. Die Erfassung und Nutzung von Informationen über den Sortimentsverbund in Handelsbetrieben. In Schirften zum Marketing, band 11.
24. Padmanabhan, B. and Tuzhilin, A. 1999. Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, Elsevier Science, 27:303-318.
25. Park, J., Chen, M., and Yu, Ph. 1995. An effective hash based algorithm for mining association rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 175-186.
26. Pessemier, E. 1980. Retail assortments - Some theoretical and applied problems. Technical Report, Marketing Science Institute Research Program.
27. Silberschatz, A. and Tuzhilin, A. 1996. What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8(6):970-974.
28. Silverstein, C., Brin, S., and Motwani, R. 1998. Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery Journal, 2(1):39-68.
29. Toivonen, H., Klemettinen, M., Ronkainen, P., Hätönen, K., and Mannila, H. 1995. Pruning and Grouping of Discovered Association Rules. In MLnet Workshop on Statistics, Machine Learning, and Discovery in Databases, Heraklion, Crete, Greece, 1995.
30. Van der Ster, W. and van Wissen, P. 1993. Marketing & Detailhandel. Wolters-Noordhoff.
31. Viveros, M., Nearhos, J., and Rothman, M. 1996. Applying data mining techniques to a health insurance information system. In Proceedings of the 22nd International Conference on Very Large Data Bases, pp. 286-294.
32. Wang, K., Tay, S.H.W., and Liu, B. 1998. Interestingness-based interval merger for numeric association rules. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98), pp. 121-127.
33. Zaki, M., Parthasarathy, S., Ogihara, M., and Li, M. 1997. New algorithms for fast discovery of association rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 283-286.