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基于谱聚类的聚类集成算法

周林 平西建 徐森 张涛

周林, 平西建, 徐森, 张涛. 基于谱聚类的聚类集成算法. 自动化学报, 2012, 38(8): 1335-1342. doi: 10.3724/SP.J.1004.2012.01335
引用本文: 周林, 平西建, 徐森, 张涛. 基于谱聚类的聚类集成算法. 自动化学报, 2012, 38(8): 1335-1342. doi: 10.3724/SP.J.1004.2012.01335
ZHOU Lin, PING Xi-Jian, XU Sen, ZHANG Tao. Cluster Ensemble Based on Spectral Clustering. ACTA AUTOMATICA SINICA, 2012, 38(8): 1335-1342. doi: 10.3724/SP.J.1004.2012.01335
Citation: ZHOU Lin, PING Xi-Jian, XU Sen, ZHANG Tao. Cluster Ensemble Based on Spectral Clustering. ACTA AUTOMATICA SINICA, 2012, 38(8): 1335-1342. doi: 10.3724/SP.J.1004.2012.01335

基于谱聚类的聚类集成算法

doi: 10.3724/SP.J.1004.2012.01335
详细信息
    通讯作者:

    周林

Cluster Ensemble Based on Spectral Clustering

  • 摘要: 谱聚类是近年来出现的一类性能优越的聚类算法,能对任意形状的数据进行聚类, 但算法对尺度参数比较敏感,利用聚类集成良好的鲁棒性和泛化能力,本文提出了基于谱聚类的聚类集成算法.该算法首先利用谱聚类算法的内在特性构造多样性的聚类成员; 然后,采用连接三元组算法计算相似度矩阵,扩充了数据点之间的相似性信息;最后,对相似度矩阵使用谱聚类算法得到最终的集成结果. 为了使算法能扩展到大规模应用,利用Nystrm采样算法只计算随机采样数据点之间以及随机采样数据点与剩余数据点之间的相似度矩阵,从而有效降低了算法的计算复杂度. 本文算法既利用了谱聚类算法的优越性能,同时又避免了精确选择尺度参数的问题.实验结果表明:较之其他常见的聚类集成算法,本文算法更优越、更有效,能较好地解决数据聚类、图像分割等问题.
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  • 收稿日期:  2011-07-11
  • 修回日期:  2011-10-17
  • 刊出日期:  2012-08-20

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