Mining non-derivable frequent itemsets over data stream

Data and Knowledge Engineering

Volumn 68, Issue 5, 2009, Pages 481-498

  Li, Haifeng ^a   Chen, Hong ^a,b  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b Renmin University of China   (China)

Author keywords

Data mining; Non derivable frequent itemsets; Stream

Indexed keywords

DATA STRUCTURES; INFORMATION MANAGEMENT;

COMPACT DATA STRUCTURES; COMPUTATIONAL COSTS; CONDENSED REPRESENTATIONS; DATA STREAMS; ITEM SETS; NON-DERIVABLE FREQUENT ITEMSETS; OPTIMIZED ALGORITHMS; SLIDING WINDOWS; STREAM; STREAM MININGS;

DATA MINING;

EID: 62849127337     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.01.002     Document Type: Article

References (42)

1. T. Asai, H. Arimura, K. Abe, S. Kawasoe, S. Arikawa, Online algorithms for mining semi-structured data stream, in: Proceedings of the ICDM'2002.
2. F. Afrati, A. Gionis, H. Mannila, Approximating a collection of frequent sets, in: Proceedings of the SIGKDD'2004.
3. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the VLDB'1994.
4. B. Babcock, S. Babu, M. Datar, R. Motwani, J. Widom, Models and issues in data stream systems, in: Proceedings of the SIGMOD'2002.
5. Boulicaut J., Bykowski A., and Rigotti C. Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery 7 (2003) 5-22
6. T. Calders, N. Dexters, B. Goethals, Mining frequent itemsets in a stream, in: Proceedings of the ICDM'2007.
7. T. Calders, B. Goethals, Mining all non-derivable frequent itemsets, in: Proceedings of the PKDD'2002.
8. T. Calders, B. Goethals, Depth-first non-derivable itemset mining, in: Proceedings of the SDM'2005.
9. T. Calders, B. Goethals, Quick inclusion-exclusion, in: Workshop KDID of PKDD'2005.
10. Calders T., and Goethals B. Non-derivable itemset mining. Data Mining and Knowledge Discovery 14 1 (2007) 71-206
11. T. Calders, B. Goethals, Minimal k-free representations of frequent sets, in: Proceedings of the PKDD'2003.
12. Chang J.H., and Lee W.S. Decaying obsolete information in finding recent frequent itemsets over data stream. IEICE Transaction on Information and Systems 87 6 (2004) 1588-1592
13. Chang J.H., and Lee W.S. A sliding window method for finding recently frequent itemsets over online data streams. Journal of Information Science and Engineering 20 4 (2004) 753-762
14. J.H. Chang, W.S. Lee, Finding recent frequent itemsets adaptively over online data streams, in: Proceedings of the SIGKDD'2003.
15. G. Cormode, F. Korn, S. Muthukrishnan, Divesh Srivastava, Finding hierarchical heavy hitters in data streams, in: Proceedings of the VLDB'2003.
16. Cheng J., Ke Y., and Ng W. A survey on algorithms for mining frequent itemsets over data streams. Knowledge and Information Systems 16 1 (2006) 1-27
17. G. Cormode, S. Muthukrishnan, What's hot and what's not: tracking most frequent items dynamically, in: Proceedings of the PODS'2003.
18. Y. Chi, H. Wang, P.S. Yu, R.R. Muntz, Moment: maintaining closed frequent itemsets over a stream sliding window, in: Proceedings of the ICDM'2004.
19. C. Giannella, J. Han, J. Pei, X. Yan, P. Yu, Mining frequent patterns in data streams at multiple time granularities, in: Proceedings of the AAAI/MIT'2003.
20. B. Goethals, J. Muhonen, H. Toivonen, Mining non-derivable association rules, in: Proceedings of the SDM'2005.
21. J. Galambos, I. Simonelli, Bonferroni-type Inequalities with Applications, Springer, 1996.
22. Han J., Cheng H., Xin D., and Yan X. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 17 (2007) 55-86
23. J. Han, J. Pei, Mining frequent patterns by pattern-growth: methodology and implications, in: Proceedings of the SIGKDD'2000.
24. R. Jin, G. Agrawal, An algorithm for in-core frequent itemset mining on streaming data, in: Proceedings of the ICDM'2005.
25. N. Jiang, L. Gruenwald, CFI-stream: mining closed frequent itemsets in data streams, in: Proceedings of the SIGKDD'2006.
26. C. Jin, W. Qian, C. Sha, J.X. Yu, A. Zhou, Dynamically maintaining frequent items over a data stream, in: Proceedings of the CIKM'2003.
27. S. Kevin, R. Ramakrishnan, Bottom-up computation of sparse and iceberg CUBEs, in: Proceedings of the SIGMOD'1999.
28. J. Koh, S. Shin, An approximate approach for mining recently frequent itemsets from data streams, in: Proceedings of the DaWak'2006.
29. C.K. Leung, Q.I. Khan, DSTree: a tree structure for the mining of frequent sets from data streams, in: Proceedings of the ICDM'2006.
30. D. Lee, W. Lee, Finding maximal frequent itemsets over online data streams adaptively, in: Proceedings of the ICDM'2005.
31. G. Liu, H. Lu, J.X. Yu, W. Wang, X. Xiao, AFOPT: an efficient implementation of pattern growth approach, in: Workshop on FIMI'2003.
32. Lucchese C., Orlando S., and Perego R. Fast and memory efficient mining of frequent closed itemsets. IEEE Transactions on Knowledge and Data Engineering 18 1 (2006) 21-36
33. Lee A.J.T., Wang C.S., Wen W.Y., Chen Y.A., and Wu H.W. An efficient algorithm for mining closed inter-transaction itemsets. Data & Knowledge Engineering 66 1 (2008) 68-91
34. G.S. Manku, R. Motwani, Approximate frequency counts over streaming data, in: Proceedings of the VLDB'2002.
35. B. Mozafari, H. Thakkar, C. Zaniolo, Verifying and mining frequent patterns from large windows over data streams, in: Proceedings of the ICDE'2008.
36. Mao G., Wu X., Zhu X., and Chen G. Mining maximal frequent itemsets from data streams. Journal of Information Science 33 3 (2007) 251-262
37. N. Pasquier, Y. Bastide, R. Taouil, L. Lakhal, Discovering frequent closed itemsets for association rules, in: Proceedings of the ICDT'1999.
38. Padmanabhan B., and Tuzhilin A. On characterization and discovery of minimal unexpected patterns in rule discovery. IEEE Transactions on Knowledge and Data Engineering 18 2 (2006) 202-216
39. W. Teng, M. Chen, P.S.Yu, A regression-based temporal pattern mining scheme for data streams, in: Proceedings of the VLDB'2003.
40. F. Tao, F. Murtagh, M. Farid, Weighted association rule mining using weighted support and significance framework, in: Proceedings of the SIGKDD'2003.
41. H.J. Woo, W.S. Lee, estMax: tracing maximal frequent itemsets over online data streams, in: Proceedings of the ICDM'2007.
42. Xin D., Han J., Yan X., and Cheng H. On compressing frequent patterns. Data & Knowledge Engineering 60 1 (2007) 5-29