Mining top-k regular-frequent itemsets using database partitioning and support estimation

Expert Systems with Applications

Volumn 39, Issue 2, 2012, Pages 1924-1936

  Amphawan, Komate ^a,b,c   Lenca, Philippe ^b,c   Surarerks, Athasit ^a  

^a CHULALONGKORN UNIVERSITY   (Thailand)

^b LAB STICC   (France)

^c UNIVERSITÉ EUROPÉENNE DE BRETAGNE   (France)

Author keywords

Association rule; Data mining; Frequent itemset; Regular frequent itemset; Top k itemset mining

Indexed keywords

BEST FIRST SEARCH; DATABASE PARTITIONING; DATABASE SCANS; EFFICIENT ALGORITHM; ESTIMATION TECHNIQUES; EXPERIMENTAL STUDIES; FREQUENT ITEMSET; INTERESTINGNESS; ITEM SETS; ITEMSET; ITEMSET MINING; REGULAR PERIODS; STATE-OF-THE-ART ALGORITHMS; SUPPORT THRESHOLD; SYNTHETIC AND REAL DATA;

ALGORITHMS;

DATABASE SYSTEMS;

EID: 80054950168     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.08.055     Document Type: Conference Paper

References (26)

1. Agrawal, R.; Imielinski, T.; Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, May 26-28 (pp. 207-216).
2. Agrawal, R.; Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In: VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 12-15 (pp. 487-499).
3. K. Amphawan, P. Lenca, and A. Surarerks Mining top-k periodic-frequent patterns without support threshold The 3rd international conference on advances in information technology, IAIT'09, Bangkok, Thailand, December 1-5 CCIS Vol. 55 2009 Springer 18 29
4. R. Bhattacharyya, and B. Bhattacharyya High confidence association mining without support pruning A. Ghosh, R.K. De, S.K. Pal, Pattern recognition and machine intelligence, proceedings of the second international conference, PReMI 2007, Kolkata, India, December 18-22, 2007 Lecture notes in computer science Vol. 4815 2007 Springer 332 340
5. Brin, S.; Motwani, R.; Ullman, J. D.; Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In: SIGMOD'97: Proceedings of the 1997 ACM SIGMOD international conference on management of data (pp. 255-264). (Pubitemid 127456790)
6. Y.-L. Cheung, and A.W.-C. Fu Mining frequent itemsets without support threshold: With and without item constraints IEEE Transactions on Knowledge and Data Engineering 16 9 2004 1052 1069
7. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, and R. Motwani Finding interesting associations without support pruning IEEE Transactions on Knowledge and Data Engineering 13 1 2001 64 78 (Pubitemid 32301044)
8. A.W.-C. Fu, R.W.w. Kwong, and J. Tang Mining n-most interesting itemsets ISMIS'00: Proceedings of the 12th international symposium on foundations of intelligent systems 2000 Springer-Verlag 59 67
9. M.M. Gaber, A. Zaslavsky, and S. Krishnaswamy Mining data streams: a review SIGMOD Record 34 2 2005 18 26 (Pubitemid 41260006)
10. B. Goethals Frequent set mining The Data Mining and Knowledge Discovery Handbook 2005 Springer 377 397
11. J. Han, H. Cheng, D. Xin, and X. Yan Frequent pattern mining: current status and future directions Data Mining and Knowledge Discovery 15 1 2007 55 86 (Pubitemid 47142811)
12. J. Han, J. Wang, Y. Lu, and P. Tzvetkov Mining top-k frequent closed patterns without minimum support IEEE International Conference on Data Mining 2002 211 218
13. J. Hipp, U. Güntzer, and G. Nakhaeizadeh Algorithms for association rule mining - a general survey and comparison SIGKDD Explorations 2 1 2000 58 64
14. Y.S. Koh Mining non-coincidental rules without a user defined support threshold Advances in knowledge discovery and data mining, 12th Pacific-Asia conference, PAKDD 2008, Osaka, Japan, May 20-23 Lecture notes in computer science Vol. 5012 2008 Springer 910 915 (Pubitemid 351776386)
15. Y. Le Bras, P. Lenca, and S. Lallich Mining interesting rules without support requirement: A general universal existential upward closure property Annals of Information Systems 8 2010 75 98
16. P. Lenca, P. Meyer, B. Vaillant, and S. Lallich On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid European Journal of Operational Research 184 2 2008 610 626 (Pubitemid 47374386)
17. H.-F. Li Interactive mining of top-k frequent closed itemsets from data streams Expert Systems with Applications 36 7 2009 10779 10788
18. H.-F. Li Mining top-k maximal reference sequences from streaming web click-sequences with a damped sliding window Expert Systems with Applications 36 8 2009 11304 11311
19. J. Li, X. Zhang, G. Dong, K. Ramamohanarao, and Q. Sun Efficient mining of high confidence association rules without support thresholds J.M. Zytkow, J. Rauch, Principles of data mining and knowledge discovery, third european conference, PKDD'99, Prague, Czech Republic, September 15-18 Lecture notes in computer science Vol. 1704 1999 Springer 406 411
20. A. Metwally, D. Agrawal, and A.E. Abbadi Efficient computation of frequent and top-k elements in data streams T. Eiter, L. Libkin, Database theory - ICDT 2005, proceedings of the 10th international conference, Edinburgh, UK, January 5-7, 2005 Lecture notes in computer science Vol. 3363 2005 Springer 398 412
21. A. Pietracaprina, and F. Vandin Efficient incremental mining of top-k frequent closed itemsets Discovery science Lecture notes in computer science Vol. 4755 2007 Springer 275 280
22. S.K. Tanbeer, C.F. Ahmed, B.-S. Jeong, and Y.-K. Lee Discovering periodic-frequent patterns in transactional databases Advances in knowledge discovery and data mining, 13th Pacific-Asia conference, PAKDD 2009, Bangkok, Thailand, April 27-30 Lecture notes in computer science Vol. 5476 2009 Springer 242 253
23. P.S. Tsai Mining top-k frequent closed itemsets over data streams using the sliding window model Expert Systems with Applications 37 10 2010 6968 6973
24. Wang, J.; Han, J.; Lu, Y.; & Tzvetkov, P. (2005). Tfp: an efficient algorithm for mining top-k frequent closed itemsets. In: Proceeding of the IEEE Transactions on Knowledge and Data Engineering (Vol. 17, pp. 652-664). Los Alamitos, CA, USA: IEEE Computer Society. (Pubitemid 40750824)
25. Wang, K.; He, Y.; & Cheung, D. W. (2001). Mining confident rules without support requirement. In CIKM'01: Proceedings of the tenth international conference on information and knowledge management (pp. 89-96). (Pubitemid 34974618)
26. B. Yang, H. Huang, and Z. Wu Topsis: Finding top-k significant n-itemsets in sliding windows adaptively Knowledge-Based Systems 21 6 2008 443 449