Arbitrary shape cluster algorithm for clustering data stream

被引：7
作者：
Department of Computer Science, Sun Yat-Sen University, Guangzhou 510275, China [1 ]
机构：
[1] Department of Computer Science, Sun Yat-Sen University
来源：
Ruan Jian Xue Bao | 2006年 / 3卷 / 379-387期
关键词：
Clustering; Data mining; Data stream;
D O I：
10.1360/jos170379
中图分类号：
学科分类号：
摘要：
CluStream is a popular data stream cluster algorithm, however, it is not capable enough to cluster arbitrary shapes and make clusters in periodic data. This paper introduces a new algorithm ACluStream to solve these problems. The ACluStream is based on the partition and assemble of the space and cluster by density. In the experiment, it is shown that ACluStream is better than CluStream in speed and accuracy.
引用
收藏
页码：379 / 387
页数：8
相关论文
共 9 条
[1]  
Golab L., Ozsu M.T., Issues in data stream management, SIGMOD Record, 32, 2, pp. 5-14, (2003)
[2]  
Babcock B., Babu S., Datar M., Motwani R., Widom J., Models and issues in data stream systems, Proc. of the 21st ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems, pp. 1-16, (2002)
[3]  
Barbara D., Requirements for clustering data streams, ACM SIGKDD Explorations Newsletter, 3, 2, pp. 23-27, (2003)
[4]  
Aggarwal C., Han J., Wang J., Yu P.S., A framework for clustering evolving data streams, VLDB 2003, pp. 81-92, (2003)
[5]  
Guha S., Mishra N., Motwani R., O'Callaghan L., Clustering data streams, FOCS 2000, pp. 359-366, (2000)
[6]  
O'Callaghan L., Mishra N., Meyerson A., Guha S., Streaming-Data algorithms for high-quality clustering, ICDE Conf., pp. 685-704, (2002)
[7]  
Zhang T., Ramakrishnan R., Livny M., BIRCH: An efficient data clustering method for very large databases, SIGMOD'96, pp. 103-114, (1996)
[8]  
Han J., Kamber M., Data Mining-Concepts and Techniques, (2001)
[9]  
Manku G.S., Motwani R., Approximate frequency counts over data streams, VLDB 2002, pp. 346-357, (2002)
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