Clustering Ensemble Algorithm Based on New Members Selection Method

Clustering ensemble is one attractive field of cluster analysis. Focus on improving the robustness and precision of clustering algorithms, clustering ensemble methods combine multiple clustering members (clustering results) of the same issue into one result. During the process of clustering ensemble, the quality of clustering members is very important, some poor clustering members will directly affect the results of clustering ensemble. In this paper, clustering ensemble algorithms are studied, clustering ensemble algorithms based on new clustering members selection method using weighted JP(Jaccard index-Precision) are proposed. Experimental results show that precision and robustness of clustering are both improved on different datasets. The clustering performance is improved significantly.