Sliding window filtering: An efficient method for incremental mining on a time-variant database

Information Systems

Volumn 30, Issue 3, 2005, Pages 227-244

  Lee, Chang Hung ^a   Lin, Cheng Ru ^a   Chen, Ming Syan ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Association rules; Data mining; Incremental mining; Sliding window filtering; Time variant database

Indexed keywords

ALGORITHMS; ASSOCIATIVE PROCESSING; COMPUTER SCIENCE; DATA MINING; DATA PROCESSING; ELECTRONIC COMMERCE; SALES; SENSITIVITY ANALYSIS; TREES (MATHEMATICS); WORLD WIDE WEB;

ASSOCIATION RULES; INCREMENTAL MINING; SLIDING-WINDOW FILTERING; TIME-VARIANT DATABASES;

DATABASE SYSTEMS;

EID: 9944236785     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2004.02.001     Document Type: Article

References (38)

1. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD, May 1993, pp. 207-216.
2. R. Agrawal, R. Srikant, Mining sequential patterns, Proceedings of the 11th International Conference on Data Engineering, March 1995, pp. 3-14.
3. J.M. Ale, G. Rossi, An approach to discovering temporal association rules, ACM Symposium on Applied Computing, 2000.
4. M.-S. Chen, J. Han, P.S. Yu, Data mining: an overview from database perspective, IEEE Trans. Knowledge Data Eng. 8 (6) (1996) 866-883.
5. M.-S. Chen, J.-S. Park, P.S. Yu, Efficient data mining for path traversal patterns, IEEE Trans. Knowledge Data Eng. 10 (2) (1998) 209-221.
6. X. Chen, I. Petr, Discovering temporal association rules: algorithms, language and system, Proceedings of 2000 International Conference on Data Engineering, 2000.
7. J. Han, G. Dong, Y. Yin, Efficient mining of partial periodic patterns in time series database, in: Proceedings of the 15th International Conference on Data Engineering, March 1999, pp. 106-115.
8. R.T. Ng, J. Han, Efficient and effective clustering methods for spatial data mining, Proceedings of the 18th Interna tional Conference on Very Large Data Bases, September 1994, pp. 144-155.
9. K. Wang, S.Q. Zhou, S.C. Liew, Building hierarchical classifiers using class proximity, Proceedings of 1999 International Conference on Very Large Data Bases, 1999, pp. 363-374.
10. C. Yang, U. Fayyad, P. Bradley, Efficient discovery of error-tolerant frequent itemsets in high dimensions, The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
11. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, Proceedings of the 20th International Conference on Very Large Data Bases, September 1994, pp. 478-499.
12. E. Cohen, et al., Finding interesting associations without support pruning, IEEE Trans. Knowledge Data Eng. (2001) 64-78.
13. J. Han, Y. Fu, Discovery of multiple-level association rules from large databases, Proceedings of the 21st International Conference on Very Large Data Bases, September 1995, pp. 420-431.
14. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, Proceedings of 2000 ACM-SIGMOD International Conference on Management of Data, May 2000, pp. 486-493.
15. B. Liu, W. Hsu, Y. Ma, Mining association rules with multiple minimum supports, Proceedings of 1999 International Conference on Knowledge Discovery and Data Mining, August 1999.
16. H. Mannila, H. Toivonen, A. Inkeri Verkamo, Efficient algorithms for discovering association rules, Proceedings of AAAI Workshop on Knowledge Discovery in Data-bases, July, 1994, pp. 181-192.
17. J.-S. Park, M.-S. Chen, P.S. Yu, Using a hash-based method with transaction trimming for mining association rules, IEEE Trans. Knowledge and Data Eng. 9 (5) (1997) 813-825.
18. A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, Proceedings of the 21st International Conference on Very Large Data Bases, September 1995, pp. 432-444.
19. K. Wang, Y. He, J. Han, Mining frequent itemsets using support constraints, Proceedings of 2000 International Conference on Very Large Data Bases, September 2000.
20. J.-L. Lin, M.H. Dunham, Mining association rules: antiskew algorithms, Proceedings of 1998 International Conference on Data Engineering, 1998, pp. 486-493.
21. R. Agarwal, C. Aggarwal, V.V.V. Prasad, A tree projection algorithm for generation of frequent itemsets, J. Parallel Distributed Comput. (special issue on High Performance Data Mining) (2000).
22. J. Han, J. Pei, Mining frequent patterns by pattern-growth: methodology and implications, ACM SIGKDD Explorations (special issue on Scalable Data Mining Algorithms), December 2000.
23. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, M.-C. Hsu, FreeSpan: frequent pattern-projected sequen-tial pattern mining, Proceedings of 2000 International Conference on Knowledge Discovery and Data Mining, August 2000, pp. 355-359.
24. J. Pei, J. Han, L.V.S. Lakshmanan, Mining frequent itemsets with convertible constraints, Proceedings of 2001 International Conference on Data Engineering, 2001.
25. J. Han, L.V.S. Lakshmanan, R.T. Ng, Constraint-based, multidimensional data mining, Computer (special issues on Data Mining) (1999) 46-50.
26. D. Kifer, C. Bucila, J. Gehrke, W. White, DualMiner: a dual-pruning algorithm for itemsets with constraints, in: Proceedings of the 8th ACM SIGKDD International Conference on knowledge discovery and data mining, 2002.
27. L.V.S. Lakshmanan, R. Ng, J. Han, A. Pang, Optimization of constrained frequent set queries with 2-variable constraints, Proceedings of 1999 ACM-SIGMOD Conference on Management of Data, June 1999, pp. 157-168.
28. J. Pei, J. Han, Can we push more constraints into frequent pattern mining? Proceedings of 2000 International Conference on Knowledge Discovery and Data Mining, August 2000.
29. A.K.H. Tung, J. Han, L.V.S. Lakshmanan, R.T. Ng, Constraint-based clustering in large databases, Proceedings of 2001 International Conference on Database Theory, January 2001.
30. J. Hipp, U. Güntzer, G. Nakhaeizadeh, Algorithms for association rule mining - a general survey and comparison, SIGKDD Explorations 2 (1) (2000) 58-64.
31. D. Cheung, J. Han, V. Ng, C.Y. Wong, Maintenance of discovered association rules in large databases: an incremental updating technique, Proceedings of 1996 International Conference on Data Engineering, February 1996, pp. 106-114.
32. D. Cheung, S.D. Lee, B. Kao, A general incremental technique for updating discovered association rules, Proceedings of the International Conference on Database Systems for Advanced Applications, April 1997.
33. S. Thomas, S. Bodagala, K. Alsabti, S. Ranka, An efficient algorithm for the incremental updation of association rules in large databases, Proceedings of 1997 International Conference on Knowledge Discovery and Data Mining 1997.
34. N.F. Ayan, A.U. Tansel, E. Arkun, An efficient algorithm to update large itemsets with early pruning, Proceedings of 1999 International Conference on Knowledge Discovery and Data Mining, 1999.
35. S. Brin, R. Motwani, J.D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data, ACM SIGMOD Rec. 26 (2) (1997) 255-264.
36. R. Srikant, R. Agrawal, Mining generalized association rules, Proceedings of the 21st International Conference on Very Large Data Bases, September 1995, pp. 407-419.
37. H. Toivonen, Sampling large databases for association rules, Proceedings of the 22nd VLDB Conference, September 1996, pp. 134-145.
38. A. Mueller, Fast sequential and parallel algorithms for association rule mining: a comparison, Technical Report CS-TR-3515, Department of Computer Science, University of Maryland, College Park, MD, 1995.