Misleading Generalized Itemset discovery

Expert Systems with Applications

Volumn 41, Issue 4 PART 1, 2014, Pages 1400-1410

  Cagliero, Luca ^a   Cerquitelli, Tania ^a   Garza, Paolo ^a   Grimaudo, Luigi ^a  

^a POLITECNICO DI TORINO   (Italy)

Author keywords

Data mining; Generalized itemset mining; Mobile data analysis; Taxonomies

Indexed keywords

DATA CORRELATIONS; EFFECTIVENESS AND EFFICIENCIES; ITEM SETS; ITEMSET; ITEMSET MINING; LEVEL OF ABSTRACTION; SYNTHETIC DATASETS;

TAXONOMIES;

DATA MINING;

EID: 84888294168     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.08.039     Document Type: Article

References (35)

1. Aggarwal, C. C., & Yu, P. S. (1998). A new framework for itemset generation. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems PODS '98 (pp. 18-24).
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th VLDB conference (pp. 487-499).
3. Agrawal, R., & Srikant, R. (1997). Mining association rules with item constraints. In KDD 1997 (pp. 67-73).
4. Agrawal, R., Imielinski, T., & Swami. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD 1993 (pp. 207-216).
5. K. Amphawan, P. Lenca, and A. Surarerks Mining top-k regular-frequent itemsets using database partitioning and support estimation Expert Systems with Applications 39 2012 1924 1936 10.1016/j.eswa.2011.08.055
6. Baralis, E., Cagliero, L., Cerquitelli, T., D'Elia, V., & Garza, P. (2010). Support driven opportunistic aggregation for generalized itemset extraction. In 5th IEEE international conference on intelligent systems (pp. 102-107).
7. Barsky, M., Kim, S., Weninger, T., & Han, J. (2011). Mining flipping correlations from large datasets with taxonomies. In Proceedings of the VLDB Endowment, vol. 5, pp. 370-381.
8. Blake, C., & Merz, C. (2012). Uci repository of machine learning databases. Available at http://archive.ics.uci.edu/ml. Last accessed: 20/07/2012.
9. S. Brin, R. Motwani, and C. Silverstein (Pubitemid 127456791)
10. Calders, T., & Goethals, B. (2002). Mining all non-derivable frequent itemsets. In Proceedings of the 6th european conference on principles of data mining and knowledge discovery PKDD '02 (pp. 74-85).
11. DBDMG (2013). Database and data mining group website: . last accessed: 20/06/2013. URL: .
12. T.F. Gharib An efficient algorithm for mining frequent maximal and closed itemsets International Journal of Hybrid Intelligent Systems 6 2009 147 153
13. Guo, L., Tan, E., Chen, S., Zhang, X., & Zhao, Y. (2009). Analyzing patterns of user content generation in online social networks. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 369-378).
14. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In SIGMOD'00 (pp. 1-12).
15. J. Han, and Y. Fu Mining multiple-level association rules in large databases IEEE Transactions on Knowledge and Data Engineering 11 1999 798 805 (Pubitemid 30518734)
16. R.J. Hilderman, and H.J. Hamilton Knowledge discovery and measures of interest 2001 Kluwer Academic Publishers Norwell, MA, USA
17. Hipp, J., Myka, A., Wirth, R., & Guntzer, U. (1998). A new algorithm for faster mining of generalized association rules. In Proceedings of the 2nd european symposium on principles of data mining and knowledge discovery (PKDD98) (pp. 74-82). (Pubitemid 128145823)
18. IBM (2009). IBM quest synthetic data generation code. URL: .
19. D. Kunkle, D. Zhang, and G. Cooperman Mining frequent generalized itemsets and generalized association rules without redundancy Journal of Computer Science & Technology 23 2008 77 102 (Pubitemid 351191241)
20. Mampaey, M., Tatti, N., & Vreeken, J. (2011). Tell me what I need to know: succinctly summarizing data with itemsets. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining KDD '11 (pp. 573-581).
21. Mehta, M., Agrawal, R., & Rissanen, J. (1996). Sliq: A fast scalable classifier for data mining. In EDBT (pp. 18-32).
22. J. Nahar, T. Imam, K.S. Tickle, and Y.-P.P. Chen Association rule mining to detect factors which contribute to heart disease in males and females Expert Systems with Applications 40 2013 1086 1093 10.1016/j.eswa.2012.08.028
23. Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In Proceedings of the 7th international conference on database theory ICDT '99 (pp. 398-416).
24. Pramudiono, I., & Kitsuregawa, M. (2004). Fp-tax: tree structure based generalized association rule mining. In DMKD '04 (pp. 60-63).
25. Savasere, A., Omiecinski, E., & Navathe, S.B. (1998). Mining for strong negative associations in a large database of customer transactions. In Proceedings of the 14th international conference on data engineering ICDE '98 (pp. 494-502).
26. Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In VLDB 1995 (pp. 407-419).
27. Sriphaew, K., & Theeramunkong, T. (2002). A new method for finding generalized frequent itemsets in association rule mining. In Proceeding of the VII international symposium on computers and communications (pp. 1040-1045).
28. K. Sriphaew, and T. Theeramunkong Fast algorithms for mining generalized frequent patterns of generalized association rules IEICE Transactions on Information and Systems 87 2004 761 770
29. Tan, P.-N., Kumar, V., & Srivastava, J. (2000). Indirect association: Mining higher order dependencies in data. In Proceedings of the 4th european conference on principles of data mining and knowledge discovery PKDD '00 (pp. 632-637).
30. Tan, P.-N., Kumar, V., & Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In ACM SIGKDD international conference on knowledge discovery and data mining (KDD'02) (pp. 32-41).
31. P.-N. Tan, M. Steinbach, and V. Kumar Introduction to data mining 2005 Addison-Wesley
32. Tatti, N. (2010). Probably the best itemsets. In Proceedings of the 16th ACM international conference on Knowledge discovery and data mining (pp. 293-302).
33. T. Wu, Y. Chen, and J. Han Re-examination of interestingness measures in pattern mining: a unified framework Data Mining and Knowledge Discovery 21 2010 371 397
34. C.-M. Wu, and Y.-F. Huang Generalized association rule mining using an efficient data structure Expert Systems with Applications 38 2011 7277 7290 10.1016/j.eswa.2010.12.023
35. Zaki, M., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In 3rd International conference on knowledge discovery and data mining (pp. 283-286).