Clustering evolving data streams over sliding windows

Ruan Jian Xue Bao/Journal of Software

Volumn 18, Issue 4, 2007, Pages 905-918

  Chang, Jian Long ^a   Cao, Feng ^a   Zhou, Ao Ying ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Clustering; Evolving data stream; Sliding window

Indexed keywords

DATA MINING; GAUSSIAN DISTRIBUTION;

CLUSTER FEATURES; DATA SETS; EVOLVING DATA STREAM; FALSE NEGATIVE; FALSE POSITIVE; SLIDING WINDOW;

CLUSTERING ALGORITHMS;

EID: 34249276762     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.1360/jos180905     Document Type: Article

References (21)

1. Aggarwal CC, Han J, Wang J, Yu PS. A framework for clustering evolving data streams. In: Freytag JC, Lockemann PC, Abiteboul S, Carey MJ, Selinger PG, Heuer A, eds. Proc. of the Int'l Conf. on Very Large Data Bases. Berlin: Morgan Kaufmann Publishers, 2003. 81-92
2. Chalaghan LO, Mishra N, Meyerson A, Guha S. Streaming data algorithms for high-quality clustering. In: Proc. of the 18th Int'l Conf. on Data Engineering. San Jose, 2002. 685-694. http://doi.ieeecomputersociety.org/10.1109/ICDE.2002.994785
3. Domingos P, Hulten C. Mining high-speed data streams. In: Proc. of the KDD. 2000. http://citeseer.ist.psu.edu/domingos00mining.html
4. Guha S, Meyerson A, Mishra N, Motwani R, Callaghan LO. Clustering data streams: Theory and practice. IEEE Trans. on Knowledge and Data Engineering, 2003,3(15):515-528.
5. Guha S, Mishra N, Motwani R, Callaghan LO. Clustering data stream. In: Proc. of the 41st Annual Symp. on Foundations of Computer Science. Redondo Beach: IEEE Computer Society, 2000. 359-366.
6. Nam H, Won S. Statistical grid-based clustering over data streams. SIGMOD Record, 2004,33(1):32-37.
7. Ordonez C. Clustering binary data streams with k-means. In: Zaki MJ, Aggarwal CC, eds. Proc. of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD). San Diego, 2003. 12-19.
8. Zhou A, Cai Z, Wei L, Qian W. M-Kernel merging: Towards density estimation over data streams. In: Proc. of the 8th Int'l Conf. on Database Systems for Advanced Applications (DASFAA). Kyoto, 2003. 285-292.
9. Aggarwal CC, Han J, Wang J, Yu PS. A framework for projected clustering of high dimensional data streams. In: Nascimento MA, Özsu MT, Kossmann D, Miller RJ, Blakeley JA, Schiefer KB, eds. Proc. of the VLDB. Toronto: Morgan Kaufmann Publishers, 2004. 852-863.
10. Babcock B, Datar M, Motwani R, Callaghan LO. Maintaining variance and k-medians over data stream windows. In: Proc. of the 22nd ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems. San Diego: ACM Press, 2003. 234-243.
11. Dai B, Huang J, Yeh M, Chen M. Clustering on demand for multiple data streams. In: Proc. of the ICDM. Brighton, 2004. 367-370.
12. Nasraoui O, Cardona C, Rojas C, Gonzlez F. TECNO-STREAMS: Tracking evolving clusters in noisy data streams with a scalable immune system learning model. In: Proc. of the 3rd IEEE Int'l Conf. on Data Mining (ICDM). Melbourne, 2003. 235-242.
13. Yang J. Dynamic clustering of evolving streams with a single pass. In: Dayal U, Ramamritham K, Vijayaraman TM, eds. Proc. of the ICDE. Bangalore, 2003. 695-697.
14. Han J, Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
15. Charikar M, Callaghan LO, Panigrahy R. Better streaming algorithms for clustering problems. In: Proc. of the 35th Annual ACM Symp. on Theory of Computing. San Diego, 2003. 30-39.
16. Beringer J, Hullermeier E. Online Clustering of Parallel Data Streams. Data and Knowledge Engineering. 2005. http://wwwiti.cs.uni-magdeburg.de/iti_dke/publications/DKE_draft.pdf
17. Cao F, Ester M, Qian W, Zhou A. Density-Based clustering over an evolving data stream with noise. In: Proc. of the SIAM Conf. on Data Mining (SDM). 2006.
18. Zhu WH, Yin J, Xie YH. Arbitrary shape cluster algorithm for clustering data stream. Journal of Software, 2006,17(3):379-387 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/17/379.htm
19. Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data stream systems. In: Popa L, ed. Proc. of the 21st ACM Symp. on Principles of Databases Systems (PODS). Madison, 2002. 1-16.
20. Zhang T, Ramakrishnan R, Livny M. Birch: An efficient data clustering method for very large databases. In: Jagadish HV, Mumick IS, eds. Proc. of the SIGMOD. Montreal: ACM Press, 1996. 103-114.
21. Datar M, Gionis A, Indyk P, Motwani R. Maintaining stream statistics over sliding windows. In: Proc. of the 13th Annual ACM-SIAM Symp. on Discrete Algorithms (SODA). San Francisco, 2002. 635-644. http://www-cs.stanford.edu/-datar/papers/sicomp_streams.pdf