On Optimal rule mining: A framework and a necessary and sufficient condition of antimonotonicity

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5476 LNAI, Issue , 2009, Pages 705-712

  Le Bras, Yannick ^a,c   Lenca, Philippe ^a,c   Lallich, Stéphane ^b  

^a LAB STICC   (France)

^b UNIVERSITÉ DE LYON   (France)

^c UNIVERSITÉ EUROPÉENNE DE BRETAGNE   (France)

Author keywords

Algorithms; Analytic; Association rules; Nuggets; Optimal rules; Pruning; Redundance

Indexed keywords

ANALYTIC; NUGGETS; OPTIMAL RULES; PRUNING; REDUNDANCE;

ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; BOOLEAN FUNCTIONS; MINING; TRAJECTORIES;

OPTIMIZATION;

EID: 67650659748     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01307-2_71     Document Type: Conference Paper

References (24)

1. Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ICMD, pp. 207-216 (1993)
2. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80-86 (1998)
3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 478-499 (1994)
4. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ICMD, pp. 1-12 (2000)
5. Goethals, B.: Frequent set mining. In: The Data Mining and Knowledge Discovery Handbook, pp. 377-397 (2005)
6. Cohen, E., Datar, M., Fujiwara, S., Gionis, A., Indyk, P., Motwani, R., Ullman, J.D., Yang, C.: Finding interesting associations without support pruning. In: TKDE, pp. 64-78 (2001) (Pubitemid 32301044)
7. Koh, Y.S.: Mining non-coincidental rules without a user defined support threshold. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS, vol.5012, pp. 910-915. Springer, Heidelberg (2008) (Pubitemid 351776386)
8. Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. In: IS, pp. 293-313 (2004)
9. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3, Article 9) (2006)
10. Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. In: EJOR, pp. 610-626 (2008) (Pubitemid 47374386)
11. Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: ICDM, pp. 369-376 (2001)
12. Pei, J., Han, J.: Can we push more constraints into frequent pattern mining? In: KDD, pp. 350-354 (2000)
13. Bayardo Jr., R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. In: ICDE, pp. 188-197 (1999)
14. Xiong, H., Tan, P.N., Kumar, V.: Mining strong affinity association patterns in data sets with skewed support distribution. In: ICDM, pp. 387-394 (2003)
15. Omiecinski, E.: Alternative interest measures for mining associations in databases. In: TKDE, pp. 57-69 (2003)
16. Wang, K., He, Y., Cheung, D.W.: Mining confident rules without support requirement. In: IKM, pp. 89-96 (2001) (Pubitemid 34974618)
17. Zimmermann, A., De Raedt, L.: CorClass: Correlated association rule mining for classification. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS, vol.3245, pp. 60-72. Springer, Heidelberg (2004) (Pubitemid 39745452)
18. Morishita, S., Sese, J.: Transversing itemset lattices with statistical metric pruning. In: PODS, pp. 226-236 (2000) (Pubitemid 30881363)
19. Bonchi, F., Lucchese, C.: Pushing tougher constraints in frequent pattern mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol.3518, pp. 114-124. Springer, Heidelberg (2005)
20. Li, J.: On optimal rule discovery. In: TKDE, pp. 460-471 (2006) (Pubitemid 43327096)
21. Hébert, C., Cŕemilleux, B.: A unified view of objective interestingness measures. In: Perner, P. (ed.) MLDM 2007. LNCS, vol.4571, pp. 533-547. Springer, Heidelberg (2007) (Pubitemid 350270686)
22. Lallich, S., Vaillant, B., Lenca, P.: Parametrised measures for the evaluation of association rule interestingness. In: AMSDA, pp. 220-229 (2005)
23. Lallich, S., Vaillant, B., Lenca, P.: A probabilistic framework towards the parameterization of association rule interestingness measures. In: MCAP, pp. 447-463 (2007)
24. Li, J., Chee Fu, A.W., He, H., Chen, J., Jin, H., McAullay, D.,Williams, G., Sparks, R., Kelman, C.: Mining risk patterns in medical data. In: KDD, pp. 770-775 (2005) (Pubitemid 43218353)