计算机科学 ›› 2011, Vol. 38 ›› Issue (5): 145-148.

• 数据库与数据挖掘 • 上一篇    下一篇

一种基于密度和滑动窗口的数据流聚类算法

胡睿,林昭文,柯宏力,马严   

  1. (北京邮电大学 北京100876)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受中央高校基木科研业务费项目(2009RC0502)资助。

DataStreams Clustering Algorithm Based on Density and Sliding Window

HU Ru,LIN Zhao-wen,KE Hong-li,MA Yan   

  • Online:2018-11-16 Published:2018-11-16

摘要: 总结目前主流数据流聚类算法的优缺点后,提出了一种新的数据流聚类算法------DsStrcam。该算法采用双层聚类框架,应用滑动窗口技术,基于密度对数据流进行动态聚类,可以挖掘具有任意形状的数据流,且能够动态掌握数据流的分布特征。

关键词: 数据流,聚类,密度,滑动窗口

Abstract: Summarizing the advantages and disadvantages of the current main datastrcams clustering algorithms, this paper presented a new datastrcams clustering algorithm-DsStrcam. The algorithm uses the I}oublclaycr clustering framework, makes use of sliding window technology, clusters the datastreams dynamically based on the density. This algorithm can mine the datastrcams with arbitrary shape and grasp distribution of datastrcams dynamically.

Key words: DataStreams, Clustering, Density, Sliding window

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