A new method for mining Frequent Weighted Itemsets based on WIT-trees

Expert Systems with Applications

Volumn 40, Issue 4, 2013, Pages 1256-1264

  Vo, Bay ^a   Coenen, Frans ^b   Le, Bac ^c  

^a Information Technology College   (Viet Nam)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c UNIVERSITY OF NATURAL SCIENCES   (Viet Nam)

Author keywords

Data mining; Frequent Weighted Itemset; Frequent weighted support; WIT FWI; WIT trees

Indexed keywords

DIFF-SETS; EFFICIENT COMPUTATION; ITEM SETS; ITEMSET; MINING FREQUENT ITEMSETS; TRANSACTION DATABASE; WIT-FWI; WIT-TREES;

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; FORESTRY; TREES (MATHEMATICS);

COMPUTATIONAL EFFICIENCY;

ALGORITHMS; COMPUTATION; DATA BASES; DATA PROCESSING; EFFICIENCY;

EID: 84870239150     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.08.065     Document Type: Article

References (21)

1. Agrawal, R.; Srikant, R. (1994). Fast algorithms for mining association rules. In: VLDB'94 (pp. 487-499).
2. Cai, C. H.; Fu, A. W.; Cheng, C. H.; Kwong, W. W. (1998). Mining association rules with weighted items. In: Proceedings of international database engineering and applications symposium (IDEAS 98) (pp. 68-77).
3. T.P. Hong, C.W. Lin, and Y.L. Wu Incrementally fast updated frequent pattern trees Expert Systems with Applications 34 2008 2424 2435
4. T.P. Hong, C.W. Lin, and Y.L. Wu Maintenance of fast updated frequent pattern trees for record deletion Computational Statistics and Data Analysis 53 2009 2485 2499
5. T.P. Hong, and C.J. Wang An efficient and effective association-rule maintenance algorithm for record modification Expert Systems with Applications 37 2010 618 626
6. T.P. Hong, Y.Y. Wu, and S.L. Wang An effective mining approach for up-to-date patterns Expert Systems with Applications 36 2009 9747 9752
7. http://fimi.cs.helsinki.fi/data/ (download on April 2005).
8. Khan, M. S.; Muyeba, M.; Coenen, F. (2008). A weighted utility framework for mining association rules. In: Proceedings of second uksim european symposium on computer modeling and simulation (pp. 87-92).
9. B. Le, H. Nguyen, T.A. Cao, and B. Vo A novel algorithm for mining high utility itemsets The first Asian conference on intelligent information and database systems 2009 IEEE 13 16
10. B. Le, H. Nguyen, and B. Vo An efficient strategy for mining high utility itemsets International Journal of Intelligent Information and Database Systems 5 2 2011 164 176
11. C.W. Lin, T.P. Hong, and W.H. Lu The Pre-FUFP algorithm for incremental mining Expert Systems with Applications 36 5 2009 9498 9505
12. Ramkumar, G. D.; Ranka, S.; Tsur, S. (1998). Weighted association rules: Model and algorithm. In: SIGKDD'98 (pp. 661-666).
13. Tao, F.; Murtagh, F.; Farid, M. (2003). Weighted association rule mining using weighted support and significance framework. In: SIGKDD'03 (pp. 661-666).
14. B. Vo, T.P. Hong, and B. Le DBV-Miner: A dynamic bit-vector approach for fast mining frequent closed itemsets Expert Systems with Applications 39 8 2012 7196 7206
15. B. Vo, and B. Le Mining traditional association rules using frequent itemsets lattice The 39th international conference on computers & industrial engineering 2009 IEEE Troyes, France 1401 1406
16. B. Vo, and B. Le Mining minimal non-redundant association rules using frequent itemsets lattice Journal of Intelligent Systems Technology and Applications 10 1 2011 92 106
17. B. Vo, and B. Le Interestingness measures for association rules: Combination between lattice and hash tables Expert Systems with Applications 38 9 2011 11630 11640
18. Wang, W.; Yang, J.; Yu, P. S. (2000). Efficient mining of weighted association rules. In: SIGKDD 2000 (pp. 270-274).
19. M.J. Zaki Mining non-redundant association rules Data Mining and Knowledge Discovery 9 3 2004 223 248
20. Zaki, M. J.; Gouda, K. (2003). Fast vertical mining using diffsets. In: SIGKDD'03 (pp. 326-335).
21. M.J. Zaki, and C.J. Hsiao Efficient algorithms for mining closed itemsets and their lattice structure IEEE Transactions on Knowledge and Data Engineering 17 4 2005 462 478