A scalable algorithm for the market basket analysis

Journal of Retailing and Consumer Services

Volumn 14, Issue 6, 2007, Pages 400-407

  Cavique, Luís ^a  

^a INSTITUTO POLITÉCNICO DE LISBOA   (Portugal)

Author keywords

Frequent itemset mining; Market basket analysis; Maximum weighted clique

Indexed keywords

ALGORITHM; DATA SET; HEURISTICS; RETAILING;

EID: 34548500564     PISSN: 09696989     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jretconser.2007.02.003     Document Type: Article

References (17)

1. Agrawal R., and Srikan R. Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Databases (1994) 478-499
2. Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A. Fast discovery of association rules. In: Fayyad U.M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. (Eds). Advances in Knowledge and Data Mining (1996), MIT Press, Cambridge MA
3. Anderson C. The Long Tail: Why the Future of Business is Selling Less of More (2006), Hyperion Books
4. Berge C. Graphs. 3rd edition (1991), North-Holland, Amsterdam
5. Berry M., and Linoff G. Data Mining Techniques for Marketing, Sales and Customer Support (1997), Wiley, New York
6. Borgelt C. An Implementation of the FP-growth Algorithm, Workshop Open Source Data Mining Software, OSDM'05, Chicago, IL (2005), ACM Press, USA pp. 1-5
7. Brin, S., Motwani, R., Ullman, J.D., Tsur, S., 1997. Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD Conference, Tucson, Arizona, 1997, pp. 255-264.
8. Cavique L., Rego C., and Themido I. A scatter search algorithm for the maximum clique problem. In: Ribeiro C., and Hansen P. (Eds). Essays and Surveys in Meta-heuristics (2002), Kluwer Academic Publishers, Dordrecht 227-244
9. Garey M.R., and Johnson D.S. Computers and Intractability: a Guide to the Theory of NP-Completeness (1979), W.H. Freeman and Company, New York
10. Han, J., Pei, J., Yin, Y., 2000. Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, United States, pp. 1-12.
11. Hughes A.M. Strategic Database Marketing (2000), McGraw-Hill, New York
12. In: Johnson D.S., and Trick M.A. (Eds). Clique, Coloring and Satisfiability, Second Implementation Challenge DIMACS (1996), AMS
13. Lin W., Alvarez S.A., and Ruiz C. Efficient Adaptive-Support Association Rule Mining for Recommender Systems, Data Mining and Knowledge Discovery Vol. 6 (2002), Kluwer Academic Publishers, Dordrecht pp. 83-105
14. SAS Software. Enterprise Miner Documentation (2000), SAS Institute
15. Soriano P., and Gendreau M. Tabu search algorithms for the maximum clique. In: Johnson D.S., and Trick M.A. (Eds). Clique, Coloring and Satisfiability, Second Implementation Challenge DIMACS (1996), AMS 221-242
16. Wang Y., Chuang Y.-L., Hsu M.-H., and Keh H.-C. A personalized recommender system for the cosmetic business. Expert Systems with Applications 26 (2005) 427-434
17. Zaki M. Scalable algorithms for association mining. IEEE Transactions on Knowledge Engineering 12 3 (2000) 372-390