Generating frequent itemsets incrementally: Two novel approaches based on Galois lattice theory

Journal of Experimental and Theoretical Artificial Intelligence

Volumn 14, Issue 2-3, 2002, Pages 115-142

  Valtchev, Petko ^a   Missaoui, Rokia ^b   Godin, Robert ^b   Meridji, Mohamed ^b  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

Author keywords

Association rule mining; Closed itemset extraction; Galois lattices; Incremental algorithms; Market basket analysis

Indexed keywords

ALGORITHMS; DATA MINING; PROBLEM SOLVING;

FREQUENT CLOSED ITEMSETS (FCI);

ARTIFICIAL INTELLIGENCE;

EID: 0036556026     PISSN: 0952813X     EISSN: None     Source Type: Journal    
DOI: 10.1080/09528130210164198     Document Type: Article

References (31)

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A., 1995, Fast discovery of association rules. In U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth (eds) Advances in Knowledge Discovery and Data Mining (Menlo Park CA: AAAI Press), pp. 307-328.
2. Agrawal, R., and Srikant, R., 1994, Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), Santiago, Chile, pp. 487-499.
3. Ayah, N., Tansel, A., and Arkun, M., 1999, An efficient algorithm to update large itemsets with early pruning. In Proceedings, KDD-99, San Diego, CA, pp. 287-291.
4. Barbut, M., and Monjardet, B., 1970, Ordre et Classification: Algèbre et combinatoire (Paris: Hacheytte).
5. Bayardo, R., and Agrawal, R., 1999, Mining the most interesting rules. In Proceedings, KDD-99, San Diego, CA, pp. 145-154.
6. Cheung, D.W., Han, J., Ng, V., and Wong, C., 1996, Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings, ICDE-96, New Orleans, LA, pp. 106-114.
7. Cheung, D.W., Lee, S.D., and Kao, B., 1997, A general incremental technique for maintaining discovered association rules. In Proceedings, DASFAA-97, Melbourne, Australia, pp. 185-194.
8. Davey, B.A., and Priestley, H.A., 1992, Introduction to lattice and order (Cambridge: University Press).
9. Feldman, R., Aumann, Y., Amir, A., and Mannila, H., 1997, Efficient algorithms for discovering frequent sets in incremental databases. In Proceedings, ACM SIGMOD Workshop DMKD-97, Tucson, AZ, pp. 59-790.
10. Ganter, B., and Wille, R., 1999, Formal Concept Analysis, Mathematical Foundations (Berlin: Springer Verlag).
11. Godin, R., and Missaoui, R., 1994, An incremental concept formation approach for learning from databases, Theoretical Computer Science, 133: 378-419.
12. Godin, R., Missaoui, R., and Alaoui, H., 1995, Incremental concept formation algorithms based on galois (concept) lattices, Computational Intelligence, 11(2): 246-267.
13. Guigues, J., and Duquenne, V., 1986, Familles minomales d'implications informatives résaultant d/un tableau de données binaires, Mathématiques et Sciences Humaines, 95: 5-18.
14. Hipp, J., Guentzer, U., and Nakhaeizadeh, G., 2000, Algorithms for association rule mining-a general survey and comparison, SIGKDD Explorations, 2(1): 58-64.
15. Knuth, D.E., 1998, The Art of Computer Programming, Vol. 3, Sorting and Searching (Reading MA: Addison-Wesley).
16. Luxenburger, M., 1991, Implications partielles dans un contexte, Mathématiques et Sciences Humaines, 29(113): 35-55.
17. Maier, D., 1983, The Theory of Relational Databases (Computer Science Press).
18. Mannila, H., Toivonen, H., and Verkamo, A., 1994, Efficient algorithms for discovering association rules. In Proceedings, AAAI Workshop KDD-94, Seattle, WA, pp. 181-192.
19. Pasquier, N., 2000, Extraction de bases pour les règles d'association à partir des itemsets fermés fréquents. In Proceedings, INFORSID-00, Lyon, France, pp. 56-77.
20. Pasquier, N., Bastide, Y., Taouil, T., and Lakhal, L., 1999a, Closed set based discovery of small covers for association rules. In Proceedings, BDA-99, Bordeaux, France, pp. 371-381.
21. Pasquier, N., Bastide, Y., Taouil, T., and Lakhal, L., 1999b, Efficient mining of association rules using closed itemset lattices, Information Systems, 24(1): 25-46.
22. Pei, J., Han, J., and Mao, R., 2000, CLOSET: an efficient algorithm for mining frequent closed itemsets. In Proceedings, ACM SIGMOD Workshop DMKD '00, Dallas, TX, pp. 21-30.
23. Pudi, V., and Haritsa, J.R., 2000, Quantifying the utility of the past in mining large databases, Information Systems, 25(5): 323-343.
24. Taouil, R., Pasquier, N., Bastide, Y., and Lakhal, L., 2000, Mining bases for association rules using closed sets. In Proceedings, ICDE-00, San Diego, CA, p. 307.
25. Thomas, S., Bodagala, S., Alsabti, K., and Ranka, S., 1997, An efficient algorithm for theincremental updation of association rules in large databases. In Proceedings, KDD-97, New Port Beach, CA, pp. 263-266.
26. Valtchev, P., 1999, An algorithm for minimal insertion in a type lttice, Computational Intelligence, 15(1): 63-78.
27. Valtchev, P., and Missaoui, R., 2001, Building concept (Galois) lattices from parts: generalizing the incremental methods. In Proceedings, ICCS-01, Stanford, CA, vol. 2120 of LNCS, pp. 290-303.
28. Valtchev, P., Missaoui, R., and Lebrun, P., 2002, A partition-based approach towards building Galois (concept) lattices, Discrete Mathematics, 256(3): 801-829.
29. Wille, R., 1982, Restructuring the lattice theory: an approach based on hierarchies of concepts. In I. Rival (cd.) Ordered Sets (Dordrecht-Boston: Reidel), pp. 445-470.
30. Zaki, M., 2000, Generating non-redundant association rules. In Proceedings, KDD-00, Boston, MA, pp. 34-43.
31. Zaki, M., and Hsiao, C.-J., 1999, CHARM: an efficient algorithm for closed association rule mining. RPI Technical Report 99-10, Rensselaer Polytechnic Institute.