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基于随机取样的选择性K-means聚类融合算法
  • 摘要

    由于缺少数据分布、参数和数据类别标记的先验信息,部分基聚类的正确性无法保证,进而影响聚类融合的性能;而且不同基聚类决策对于聚类融合的贡献程度不同,同等对待基聚类决策,将影响聚类融合结果的提升.为解决此问题,提出了基于随机取样的选择性K-means聚类融合算法(RS-KMCE).该算法中的随机取样策略可以避免基聚类决策选取陷入局部极小,而且依据多样性和正确性定义的综合评价值,有利于算法快速收敛到较优的基聚类子集,提升融合性能.通过2个仿真数据库和4个UCI数据库的实验结果显示:RS-KMCE的聚类性能优于K-means 算法、K-means融合算法(KMCE)以及基于Bagging的选择性K-means聚类融合(BA-KMCE).

  • 作者

    王丽娟  郝志峰  蔡瑞初  温雯  WANG Lijuan  HAO Zhifeng  CAI Ruichu  WEN Wen 

  • 作者单位

    华南理工大学计算机科学与工程学院,广州51006/华南理工大学计算机科学与工程学院,广州51006;广东工业大学计算机学院,广州510006/广东工业大学计算机学院,广州,510006

  • 刊期

    2013年7期 ISTIC PKU

  • 关键词

    聚类融合  选择性聚类融合  随机取样  聚类决策评价  K-means  clustering ensemble  selective clustering ensemble  random sampling  evaluation index of clustering  K-means 

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