Handling dynamic weights in weighted frequent pattern mining

IEICE Transactions on Information and Systems

Volumn E91-D, Issue 11, 2008, Pages 2578-2588

  Ahmed, Chowdhury Farhan ^a   Tanbeer, Syed Khairuzzaman ^a   Jeong, Byeong Soo ^a   Lee, Young Koo ^a  

^a Kyung Hee University   (South Korea)

Author keywords

Data mining; Dynamic weight; Knowledge discovery; Weighted frequent pattern mining

Indexed keywords

COSTS; SEMANTICS;

DYNAMIC WEIGHT; FREQUENT PATTERN MINING; HANDLING DYNAMICS; MINING TECHNIQUES; PERFORMANCE ANALYSIS; REAL-WORLD SCENARIO; STREAM DATA MINING; WEIGHTED FREQUENT PATTERN MINING;

DATA MINING;

EID: 68749107028     PISSN: 09168532     EISSN: 17451361     Source Type: Journal    
DOI: 10.1093/ietisy/e91-d.11.2578     Document Type: Article

References (30)

1. R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," Proc. 12th ACM SIGMOD Int. Conf. on Management of Data, pp.207-216, May 1993.
2. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," Proc. 20th Int. Conf. on Very Large Data Bases, pp.487-499, Sept. 1994.
3. U. Yun and J.J. Leggett, "WFIM: Weighted frequent itemset mining with a weight range and a minimum weight," Proc. Fourth SIAM Int. Conf. on Data Mining, pp.636-640, USA, 2005.
4. U. Yun and J.J. Leggett, "WLPMiner: Weighted frequent pattern mining with length decreasing support constraints," Proc. 9th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), pp.555-567, Vietnam, 2005.
5. U. Yun, "Efficient mining of weighted interesting patterns with a strong weight and/or support affinity," Inf. Sci., vol.177, pp.3477-3499, 2007.
6. C.H. Cai, A.W. Fu, C.H. Cheng, and W.W. Kwong, "Mining association rules with weighted items," Proc. Int. Database Engineering and Applications Symposium, IDEAS 98, pp.68-77, Cardiff, Wales, UK, 1998.
7. F. Tao, "Weighted association rule mining using weighted support and significant framework," Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.661-666, USA, 2003.
8. W. Wang, J. Yang, and P.S. Yu, "WAR: Weighted association rules for item intensities," Knowledge Information and Systems, vol.6, pp.203-229, 2004.
9. J. Han, J. Pei, Y. Yin, and R. Mao, "Mining frequent patterns without candidate generation: A frequent-pattern tree approach," Data Mining and Knowledge Discovery, vol.8, pp.53-87, 2004.
10. G. Grahne and J. Zhu, "Fast algorithms for frequent itemset mining using FP-Trees," IEEE Trans. Knowl. Data Eng., vol.17, no.10, pp.1347-1362, Oct. 2005.
11. J. Han, H. Cheng, D. Xin, and X. Yan, "Frequent pattern mining: Current status and future directions," Data Mining and Knowledge Discovery, vol.15, pp.55-86, 2007.
12. J. Wang, T. Fukasawa, S. Urabe, T. Takta, and M. Miyazaki, "Mining frequent patterns securely in distributed systems," IEICE Trans. Inf. & Syst., vol.E89-D, no.11, pp.2739-2747, Nov. 2006.
13. C. Raissi, P. Poncelet, and M. Teisseire, "Towards a new approach for mining frequent itemsets on data stream," Journal of Intelligent Information Systems, vol.28, pp.23-36, 2007.
14. A. Metwally, D. Agrawal, and A.E. Abbadi, "An integrated efficient solution for computing frequent and top-k elements in data streams," ACM Trans. Database Systems (TODS), vol.31, no.3, pp.1095-1133, 2006.
15. N. Jiang and L. Gruenwald, "Research issues in data stream association rule mining," SIGMOD Record, vol.35, no.1, pp.14-19, March 2006.
16. C.K.-S. Leung and Q.I. Khan, "DSTree: A tree structure for the mining of frequent sets from data streams," Proc. 6th Int. Conf. on Data Mining (ICDM'06), pp.928-932, 2006.
17. J.-L. Koh and S.-F. Shieh, "An efficient approach for maintaining association rules based on adjusting FP-tree structures," Proc. DASFAA'04, pp.417-424, 2004.
18. X. Li, Z.-H. Deng, and S. Tang, "A fast algorithm for maintenance of association rules in incremental databases," Advanced Data Mining and Applications (ADMA 06), vol.4093, pp.56-63, July 2006.
19. C.K.-S. Leung, Q.I. Khan, Z. Li, and T. Hoque, "CanTree: A canonical-order tree for incremental frequent-pattern mining," Knowledge and Information Systems, vol.11, no.3, pp.287-311, 2007.
20. D.W. Cheung, J. Han, V.T. Ng, and C.Y. Wong, "Maintenance of discovered association rules in large databases: An incremental updating technique," Proc. 12th International Conference on Data Engineering, pp.106-114, 1996.
21. H. Xiong, P.-N. Tan, and V. Kumar, "Hyperclique pattern discovery," Data Mining and Knowledge Discovery, vol.13, pp.219-242, 2006.
22. Frequent itemset mining dataset repository. Available from http://fimi.cs.helsinki.fi/data/
23. UCI machine learning repository. Available from http://kdd.ics.uci.edu/
24. J. Pisharath, Y. Liu, J. Parhi, W.-K. Liao, A. Choudhary, and G. Memik, NU-MineBench version 2.0 source code and datasets. Available from: http://cucis.ece.northwestern.edu/projects/DMS/MineBench.html
25. T. Brijs, G. Swinnen, K. Vanhoof, and G. Wets, "Using association rules for product assortment decisions: A case study," Proc. Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.254-260, 1999.
26. Y. Liu, W.-K. Liao, and A. Choudhary, "A fast high utility itemsets mining algorithm," Proc. 1st International Conf. on Utility-Based Data Mining, pp.90-99, Aug. 2005.
27. Y.-C. Li, J.-S. Yeh, and C.-C. Chang, "Isolated items discarding strategy for discovering high utility itemsets," Data & Knowledge Engineering, vol.64, pp.198-217, 2008.
28. U. Yun, "Mining lossless closed frequent patterns with weight constraints," Knowledge-Based Systems, vol.210, pp.86-97, 2007.
29. S. Zhang, C. Zhang, and X. Yan, "Post-mining: Maintenance of association rules by weighting," Information Systems, vol.28, pp.691-707, 2003.
30. S.K. Tanbeer, C.F. Ahmed, B.-S. Jeong, and Y.-K. Lee, "CP-tree: A tree structure for single pass frequent pattern mining," Proc. 12th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), pp.1022-1027, 2008.