Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 457-460.doi: 10.11896/JsJkx.190700044

• Database & Big Data & Data Science • Previous Articles     Next Articles

Selective Clustering Ensemble Based on Xie-Beni Index

SHAO Chao and MA Jin-Jia   

  1. School of Computer & Information Engineering,Henan University of Economics and Law,Zhengzhou 450046,China
  • Published:2020-07-07
  • About author:SHAO Chao, born in 1977, professor, is a member of China Computer Federation.His main research interests include machine learning and so on.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61806073,61907011).

Abstract: Selective clustering ensemble is to select some of the basic clustering results with high accuracy and large diversity for integration,so as to obtain more effective clustering ensemble results.In the cluster analysis application,the cluster validity index is used to measure the goodness of the clustering results.In this paper,a selective clustering ensemble algorithm based on Xie-Beni index is proposed.The algorithm uses Xie-Beni index to measure the validity of the basic clustering results,and uses NMI(normalized mutual information) to select the better basic clustering results to enhance the aggregation,thereby improving the accuracy of the clustering results.Experimental results confirm the effectiveness of the algorithm.

Key words: Clustering validity index, NMI, Selective clustering ensemble, Xie-beni

CLC Number: 

  • TP181
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