SeqStream: Mining closed sequential patterns over stream sliding windows

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 83-92

  Lei, Chang ^a,b,c   Tengjiao, Wang ^a,b   Dongqing, Yang ^a,b   Hua, Luan ^d  

^a PEKING UNIVERSITY   (China)

^b Peking University   (China)

^c EMC Research China   (China)

^d RENMIN UNIVERSITY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED PATTERN; CLOSED SEQUENCES; CLOSED SEQUENTIAL PATTERNS; COMPUTATION POWER; DATA STREAM; EFFICIENT ALGORITHM; FREQUENT PATTERNS; NOVEL STRATEGIES; ORDERS OF MAGNITUDE; PREFIXSPAN; SEARCH SPACES; SLIDING WINDOW; SYNTHETIC DATASETS;

DATA COMMUNICATION SYSTEMS; DATA MINING; INFORMATION MANAGEMENT; SEQUENTIAL SWITCHING; WINDOWS;

MINING;

EID: 67049119058     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.36     Document Type: Conference Paper

References (23)

1. R. Agrawal and R. Srikant. Sequential pattern mining using a bitmap representation. ICDE, 1995.
2. R. Agrawal and R. Srikant. Mining sequential patterns: generalizations and performance improvements. EDBT, 1996.
3. J. Ayres, J. Flannick, J. Gehrke, and T. Yiu. Sequential pattern mining using a bitmap representation. SIGKDD, 2002.
4. B. Babcock and S. Babu et al. Models and issues in data stream systems. PODS, 2002.
5. B. Babcock and M. Datar et al. Maintaining variance and k-medians over data stream windows. PODS, 2003.
6. J. H. Chang and W. S. Lee. Efficient mining method for retrieving sequential patterns over online data streams. Joural of Information Science, 31(5), 2005.
7. G. Chen, X. Wu, and X. Zhu. Sequential pattern mining in multiple streams. ICDM, 2005.
8. H. Cheng, X. Yan, and J. Han. IncSpan: incremental mining of sequential patterns in large database. SIGKDD, 2004.
9. C. Ezeife and M. Monwar. SSM: a frequent sequential data stream patterns miner. CIDM, 2007.
10. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy. Mining data stream: a review. SIGMOD Record, 34(2), 2005.
11. C.-C. Ho, H.-F. Li, F.-F. Kuo, and S.-Y. Lee. Incremental mining of sequential patterns over a stream sliding window. ICDM - Workshops, 2006.
12. J. Huang, C. Tseng, J. Ou, and M. Chen. On progressive sequential pattern mining. CIKM, 2006.
13. B. Kao and M. Zhang et al. Efficient algorithms for mining and incremental update of maximal frequent sequences. Data Mining and Knowledge Discovery, 2005.
14. M. Lin and S. Lee. Incremental update on sequential patterns in large databases by implicit merging and efficient counting. Information System, 2004.
15. A. Marascu and F. Masseglia. Mining sequential patterns from data streams: a centroid approach. Journal of Intelligent Information Systems, 27(3), 2006.
16. F. Masseglia, P. Poncelet, and M. Teisseire. Incremental mining of sequential patterns in large databases. Data & Knowledge Engineering, 2003.
17. S. Parthasarathy, M. Zaki, M. Ogihara, and S. Dwarkadas. Incremental and interactive sequence mining. CIKM, 1999.
18. J. Pei and J. Han et al. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. ICDE, 2001.
19. C. Raïssi and P. Poncelet. Sampling for sequential pattern mining: from static databases to data streams. ICDM, 2007.
20. C. Raïssi, P. Poncelet, and M. Teisseire. Need for speed : mining sequential patterns in data streams. BDA, 2005.
21. J. Wang and J. Han. BIDE: efficient mining of frequent closed sequences. ICDE, 2004.
22. X. Yan, J. Han, and R. Afshar. CloSpan: mining closed sequential patterns in large datasets. SDM, 2003.
23. M. Zaki. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning, 42(1/2), 2001.