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  • 标题:Dynamic Clustering Of High Speed Data Streams
  • 本地全文:下载
  • 作者:J. Chandrika ; K.R. Ananda Kumar
  • 期刊名称:International Journal of Computer Science Issues
  • 印刷版ISSN:1694-0784
  • 电子版ISSN:1694-0814
  • 出版年度:2012
  • 卷号:9
  • 期号:2
  • 出版社:IJCSI Press
  • 摘要:We consider the problem of clustering data streams. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating but a constant time delay. The purpose of this study is to analyze the working of popular algorithms on clustering data streams and make a comparative analysis.
  • 关键词:Data streams; Unsupervised learning; Partitional clustering; Hierarchical clustering
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