Distributed data stream clustering: A fast EM-based approach

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2007, Pages 736-745

  Zhou, Aoying ^a   Cao, Feng ^a   Yan, Ying ^a   Sha, Chaofeng ^a   He, Xiaofeng ^b,c  

^a FUDAN UNIVERSITY   (China)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^c Alexa Internet ^*

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; MAXIMUM LIKELIHOOD; PROBLEM SOLVING; SYSTEMS ANALYSIS;

COMMUNICATION COST; CONSTRAINING FACTORS; CPU TIME; DATA RECORDS; DATA STREAM CLUSTERING; EM-BASED APPROACH; MEMORY CONSUMPTION;

DATA STRUCTURES;

EID: 34548773378     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2007.367919     Document Type: Conference Paper

References (24)

1. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proc. of VLDB, 2003.
2. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for projected clustering of high dimensional data streams. In Proc. of VLDB, 2004.
3. A.P.Dempster, N.M.Laird, and D.B.Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal statistical Society, Series B, 39(1):1-38, 1977.
4. B. Babcock, M. Datar, R. Motwani, and L. O'Callaghan. Maintaining variance and k-medians over data stream windows. In Proc. of PODS, 2003.
5. B. Babcock and C. Olston. Distributed top-k monitoring. In Proc. of SIGMOD, 2003.
6. P. Bradley, C. Reina, and U. Fayyad. Clustering very large databases using em mixture models. In 15th International Conference on Pattern Recognition (ICPR'00), 2000.
7. G. Cormode, M. Garofalakis, S. Muthukrishnan, and R. Rastogi. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In Proc. of SIGMOD, 2005.
8. G. Cormode and S. Muthukrishnan. What's new: Finding significant differences in network data streams. In Proc. of INFOCOM, 2004.
9. B. Dai, J. Huang, M. Yeh, and M. Chen. Clustering on demand for multiple data streams. In Proc. of ICDM, pages 367-370, 2004.
10. A. Das, S. Ganguly, M. Garofalakis, and R. Rastogi. Distributed set-expression cardinality estimation. In Proc. of VLDB, 2004.
11. G. Forman and B. Zhang. Distributed data clustering can be efficient and exact. SIGKDD Explorations, 2(2):34-38, 2000.
12. C. Glymour, D. Madigan, D. Pregibon, and P. Smyth. Statistical themes and lessons for data mining. Data Mining and Knowledge Discovery, 1(1), 1997.
13. S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams:theory and practice. In IEEE TKDE, pages 515-528, 2003.
14. S. Guha, N. Mishra, R. Motwani, andL. O'Callaghan. Clustering data stream. In Proc. of FOCS, 2000.
15. E. Januzaj, H.-P. Kriegel, and M. Pfeifle. Towards effective and efficient distributed clustering. In Work-shop on Clustering Large Data Sets (ICDM2003), 2003.
16. E. Januzaj, H. P. Kriegel, and M. Pfeifle. Scalable density based distributed clustering. In Proc. of EDBT, pages 88-105, 2004.
17. D. Kifer, S. Ben-David, and J. Gehrke. Detecting change in data streams. In Proc. of VLDB, 2004.
18. H.-P. Kriegel, P. Kroger, A. Pryakhin, and M. Schubert. Effective and efficient distributed model-based clustering. In Proc. of ICDM, pages 258-265, 2005.
19. J. A. Nelder and R. Mead. A simplex method for function minimiztion. The Computer Jouranl, 7(4):308-313, January, 1965.
20. R. D. Nowak. Distributed em algorithms for density estimation and clustering in sensor network. In IEEE Trans. on Signal Processing, 2003.
21. C. Olston, J. Jiang, and J. Widom. Adaptive filters for continuous queries over distributed data streams. In Proc. of SIGMOD, 2003.
22. H. Simon. Models of man. New York, 1957.
23. N. Ueda, R. Nakano, Z. Ghahramani, and G. E. Hinton. Smem algorithm for mixture models. Neural Computation, 12(9):2109-2128, 2000.
24. J. Yang. Dynamic clustering of evolving streams with a single pass. In Proc. of ICDE, 2003.