Clustering ensemble selection for categorical data based on internal validity indices

作者：

Highlights：

• Propose a clustering ensemble selection algorithm for categorical data(SIVID).

• SIVID measures the quality of base clusterings with internal validity indices.

• SIVD measures the diversity of base clusterings with NMI.

• Experimental results show the effectiveness and robustness of the proposed algorithm.

摘要

•Propose a clustering ensemble selection algorithm for categorical data(SIVID).•SIVID measures the quality of base clusterings with internal validity indices.•SIVD measures the diversity of base clusterings with NMI.•Experimental results show the effectiveness and robustness of the proposed algorithm.

论文关键词：Clustering ensemble selection,Categorical data,Clustering validity indices,Quality,Diversity

论文评审过程：Received 17 August 2016, Revised 16 March 2017, Accepted 17 April 2017, Available online 18 April 2017, Version of Record 25 April 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.04.019