基于Xie-Beni指数的选择性聚类集成  

Selective Clustering Ensemble Based on Xie-Beni Index

作　　者：邵超 马进家 SHAO Chao;MA Jin-Jia(School of Computer&Information Engineering,Henan University of Economics and Law,Zhengzhou 450046,China)

机构地区：[1]河南财经政法大学计算机与信息工程学院,郑州450046

出　　处：《计算机科学》2020年第S01期457-460,共4页Computer Science

基　　金：国家自然科学基金(61806073,61907011)。

摘　　要：选择性聚类集成是选择一部分精度高、差异性大的基聚类结果进行集成,从而得到更为有效的聚类集成结果。然而,聚类结果的准确性难以客观度量。为此,文中提出了一种基于Xie-Beni指数的选择性聚类集成算法,该算法采用Xie-Beni指数来度量基聚类结果的有效性,利用并结合NMI(互信息)选择出精度较高的基聚类结果,从而提升聚类结果的准确性。实验结果证实了该算法的有效性。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.

关 键 词：选择性聚类集成 聚类有效性指数 Xie-beni NMI 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]