An efficient algorithm for mining high utility itemsets with negative item values in large databases

Applied Mathematics and Computation

Volumn 215, Issue 2, 2009, Pages 767-778

  Chu, Chun Jung ^a   Tseng, Vincent S ^b   Liang, Tyne ^a  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Association rules; High utility itemsets; Utility mining

Indexed keywords

EFFICIENT ALGORITHM; EXECUTION TIME; EXPERIMENTAL CONDITIONS; EXPERIMENTAL EVALUATION; HIGH UTILITY ITEMSETS; ITEM SETS; ITEMSET; LARGE DATABASE; MEMORY SPACE; NOVEL METHODS; UTILITY MINING;

ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; MINES;

MINING;

EID: 68949101473     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2009.05.066     Document Type: Article

References (22)

1. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in Proceedings of 1993 ACM SIGMOD Intl. Conf. on Management of Data, Washington, DC, May 1993, pp. 207-216.
2. Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A.I. Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining (1996), AAAI/MIT Press, 1996 307-328
3. R. Agrawal, R. Srikant, Mining sequential patterns, in: Proceedings of the 11th International Conference on Data Engineering, March 1995, pp. 3-14.
4. N.F. Ayn, A.U. Tansel, E. Arun, An efficient algorithm to update large itemsets with early pruning, Technical Report BU-CEIS-9908, Department of CEIS Bilkent University, June 1999.
5. N.F. Ayn, A.U. Tansel, E. Arun, An efficient algorithm to update large itemsets with early pruning, in: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, August 1999.
6. C. Bettini, X.S. Wang, S. Jajodia, Testing complex temporal relationships involving multiple granularities and its application to data mining, in: Proc. of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 3-5, 1996, Montreal, Canada, ACM Press, 1996, pp. 68-78.
7. R. Chan, Q. Yang, Y. Shen, Mining high utility itemsets, in: Proceedings of IEEE ICDM, Florida, 2003.
8. D. Cheung, J. Han, V. Ng, C.Y. Wong, Maintenance of discovered association rules in large databases: an incremental updating technique, in: Proceedings of 1996 International Conference on Data Engineering, February 1996, pp. 106-114.
9. D. Cheung, S.D. Lee, B. Kao, A general incremental technique for updating discovered association rules, in: Proceedings of the International Conference on Database Systems For Advanced Applications, April 1997.
10. Y. Chi, H. Wang, P.S. Yu, R. Richard, Muntz: moment: maintaining closed frequent itemsets over a stream sliding window, in: Proceedings of the 2004 IEEE International Conference on Data Mining (ICDM'04).
11. G. Das, K.I. Lin, H. Mannila, G. Renganathan, P. Smyth, Rule discovery from time series, in: Proceedings of the 4th ACM SIGKDD, August 1998, pp. 16-22.
12. C.H. Lee, C.R. Lin, M.S. Chen, Sliding-window filtering: an efficient algorithm for incremental mining, in: International Conference on Information and Knowledge Management (CIKM01), November 2001, pp. 263-270.
13. J.L. Lin, M.H. Dunham, Mining association rules: anti-skew algorithms, in: Proceedings of 1998 International Conference on Data Engineering, 1998, pp. 486-493.
14. Y. Liu, W. Liao, A.A Choudhary, Fast high utility itemsets mining algorithm, in: Proceedings of the Utility-Based Data Mining Workshop, August 2005.
15. G.S. Manku, R. Motwani, Approximate frequency counts over data streams, in: Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China, 2002.
16. Park J.S., Chen M.S., and Yu P.S. Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering 9 5 (1997) 813-825
17. A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, in: Proceedings of the 21th International Conference on Very Large Data Bases, September 1995, pp. 432-444.
18. W.G. Teng, M.S. Chen, P.S. Yu, A regression-based temporal pattern mining scheme for data streams, in: Proceedings of the 29th International Conference on Very Large Data Bases, September 2003, pp. 93-104.
19. W.G. Teng, M.S. Chen, P.S. Yu, Resource-aware mining with variable granularities in data streams, in: Proceedings of the 4th SIAM International Conference on Data Mining, Florida, USA, 2004.
20. V.S. Tseng, C.J. Chu, T. Liang, Efficient mining of temporal high utility itemsets from data streams, in: Proceedings of ACM KDD Workshop Utility-Based Data Mining (UBDM'06), Philadelphia, Pennsylvania, USA, August, 2006.
21. V.S. Tseng, C.J. Chu, T. Liang, An efficient method for mining temporal emerging itemsets from data streams, in: International Computer Symposium (ICS), Workshop on Software Engineering, Databases and Knowledge Discovery, Taipei, Taiwan, December 2006.
22. H. Yao, H.J. Hamilton, C.J. Butz, A foundational approach to mining itemset utilities from databases, in: Proceedings of the 4th SIAM International Conference on Data Mining, Florida, USA, 2004.