Filtered-top-k association discovery

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 1, Issue 3, 2011, Pages 183-192

  Webb, Geoffrey I ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION DISCOVERIES;

DATA MINING;

EID: 84861738894     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.28     Document Type: Article

References (50)

1. Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Jorge B, Bocca MJ, Carlo Z, eds. Proceedings of the 20th International Conference on Very Large Databases VLDB '94. Santiago, Chile: Morgan Kaufmann; 1994, 487-499.
2. Agresti A. Categorical data analysis. New York:Wiley- Interscience; 2002.
3. Cohen E, Datar M, Fujiwara S, Gionis A, Indyk P, Motwani R, Ullman JD, Yang C. Finding interesting associations without support pruning. Knowledge and Data Engineering. 2001, 13:64-78.
4. Webb GI. Magnum Opus. Melbourne: GI Webb & Associates; 2010.
5. Blackard JA, Dean DJ. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Comput Electron Agric 1999, 24:131-151.
6. Piatetsky-ShapiroG.Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro G, Frawley J, eds. Knowledge Discovery in Databases. Menlo Park, CA: AAAI/MIT Press; 1991, 229-248.
7. Silberschatz A, Tuzhilin A. What makes patterns interesting in knowledge discovery systems. IEEE Trans Knowledge Data Eng 1996, 8:970-974.
8. Bayardo JRJ, Agrawal R. Mining the most interesting rules. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99) 1999: 145-154.
9. Freitas AA. On rule interestingness measures. Knowledge-Based Syst 1999, 12:309-315.
10. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo A. Finding interesting rules from large sets of discovered association rules. Proceedings of the Third International Conference on Information and Knowledge Management 1999: 401-407.
11. Liu B, Hsu W, Chen S, Ma Y. Analyzing the subjective interestingness of association rules. IEEE Intell Syst 2000, 15:47-55.
12. Sese J, Morishita S. Answering the most correlated N association rules efficiently. Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'02) 2002: 410-422.
13. Tan PN, Kumar V, Srivastava J. Selecting the right interestingness measure for association patterns. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002). Alberta, Canada: Edmonton; 2002, 491-502.
14. Calders T, Goethals B. Non-derivable itemset mining. Data Mining Knowledge Discov 2007, 14:171-206.
15. Jaroszewicz S, Scheffer T, Simovici D. Scalable pattern mining with Bayesian networks as background knowledge. Data Mining Knowledge Discov 2009, 18:56-100.
16. Hämäläinen W., Efficient discovery of the top-k optimal dependency rules with the Fisher's exact test of significance. In: Webb GI, Liu B, Zhang C, Gunopulos D, Wu X, eds. Proceedings of the Tenth IEEE International Conference on Data Mining. Sydney, Australia; 2010, 196-205.
17. Wu T, Chen Y, Han J. Re-examination of interestingness measures in pattern mining: a unified framework. Data Mining Knowledge Discov 2010, 21:371-397.
18. Bastide Y, Pasquier N, Taouil R, Stumme G, Lakhal L. Mining minimal non-redundant association rules using frequent closed itemsets. First International Conference on Computational Logic - CL 2000. Berlin: Springer-Verlag; 2000, 972-986.
19. Zaki MJ. Mining non-redundant association rules. Data Mining Knowledge Discov 2004, 9:223-248.
20. Webb GI. Discovering significant rules. In: Ungar L, Craven M, Gunopulos D, Eliassi-Rad T, eds. Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2006. The Association for Computing Machinery; 2006, 434-443.
21. Webb GI. Discovering significant patterns. Machine Learn 2007, 68:1-33.
22. Webb GI. OPUS: an efficient admissible algorithm for unordered search. J Artif Intell Res 1995, 3:431-465.
23. Webb GI. Efficient search for association rules. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and DataMining (KDD-2000). Boston, MA: The Association for Computing Machinery; 2000, 99-107.
24. Webb GI, Zhang S. Further pruning for efficient association rule discovery. Proceedings of the 14th Australian Joint Conference on Artificial Intelligence. Berlin: Springer; 2001, 605-618.
25. Han J, Wang J, Lu Y, Tzvetkov P. Mining top-k frequent closed patterns without minimum support. Int Conf Data Mining 2002, 211-218.
26. Webb GI, Zhang S. Removing trivial associations in association rule discovery. Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems. Geelong: NAISO Academic Press; 2002.
27. Webb GI, Zhang S. K-optimal rule discovery. Data Mining Knowledge Discov 2005, 39-79.
28. Wang J, Han J, Lu Y, Tzvetkov P. TFP: an efficient algorithm for mining top-k frequent closed itemsets. IEEE Trans Knowledge Data Eng 2005, 652-664.
29. Fu AWC, Renfrew WK, Tang J. Mining n-most interesting itemsets. Proceedings of the 12th International Symposium on Foundations of Intelligent Systems 2000, 59-67.
30. Pietracaprina A, Riondato M, Upfal E, Vandin F. Mining top-k frequent itemsets through progressive sampling. Data Mining Knowledge Discov 2010, 21:310-326.
31. Webb GI. Layered critical values: a powerful directadjustment approach to discovering significant patterns. Machine Learn 2008, 71:307-323.
32. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979, 6:65-70.
33. Megiddo N, Srikant R. Discovering predictive association rules. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). Menlo Park, CA: AAAI Press; 1998, 27-78.
34. Gionis A, Mannila H, Mielikinen T, Tsaparas P. Assessing data mining results via swap randomization. ACM Trans Knowledge Discov Data (TKDD) 2007, 1:11-32.
35. Asuncion A, Newman DJ. UCI machine learning repository. Irvine, California: University of California, School of Information and Computer Sciences; 2010.
36. Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. Proceedings 2000 ACMSIGMOD International Conference on Management of Data (SIGMOD'00). Dallas, TX; 2000, 1-12.
37. Pei J, Han J, Mao R. CLOSET: an efficient algorithm for mining frequent closed itemsets. Proceedings 2000 ACM-SIGMOD InternationalWorkshop on Data Mining and Knowledge Discovery (DMKD'00). Dallas, TX; 2000, 21-30.
38. Gouda K, Zaki MJ. Efficiently mining maximal frequent itemsets. First IEEE International Conference on Data Mining. San Jose, CA; 2001, 163-170.
39. Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. Proceedings of the Seventh International Conference onDatabase Theory (ICDT'99). Jerusalem, Israel; 1999, 398-416.
40. Zaki MJ, Hsiao CJ. CHARM: an efficient algorithm for closed itemset mining. Proceedings of the Second SIAM International Conference on Data Mining 2002, 457-473.
41. Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann; 1995, 432-444.
42. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI. Fast discovery of association rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in KnowledgeDiscovery and Data Mining. Menlo Park, CA: AAAI Press; 1996, 307-328.
43. Goethals B, Muhonen J, Toivonen H. Mining nonderivable association rules. Proceedings of the Fifth SIAM International Conference on Data Mining SDM- 05 2005, 239-249.
44. Liu B, Hsu W, MaY. Identifying non-actionable association rules. Proceedings of the Seventh ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining (KDD-2001). San Francisco, CA; 2001, 329-334.
45. Webb GI. Self-sufficient itemsets: an approach to screening potentially interesting associations between items. Trans Knowledge Discov Data. 2010, 3: 1-3.
46. Tatti N. Maximum entropy based significance of itemsets. Knowledge Inform Syst 2008, 17:57-77.
47. Tatti N, Mampaey M. Using background knowledge to rank itemsets. Data Mining Knowledge Discov 2010, 21:293-309.
48. Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and DataMining (KDD-98). New York: AAAI; 1998, 80-86.
49. Guha S, Rastogi R, Shim K. Rock: a robust clustering algorithm for categorical attributes. Inform Syst 2000, 25:345-366.
50. Flach P, Lavrac N. The role of feature construction in inductive rule learning. In: Raedt LD, Kramer S, eds. Proceedings of the ICML2000 Workshop on Attribute- Value and Relational Learning: Crossing the Boundaries. 2000, 1-11.