Comparison of Cluster Ensembles Methods Based on Hierarchical Clustering

Cluster ensembles method is considered as a robust and accurate alternative to single clustering runs. It mainly consists of both generation of individual member and fusion methods. In this paper, we study the cluster ensembles where individual members are obtained based on k-means clustering algorithm and fusion method of hierarchical clustering is used. Three consensus functions, which are single linkage, complete linkage and average linkage, respectively, is studied and discussed in hierarchical clustering fusion. For evaluating performance of cluster ensembles, adjusted rand index is considered. Experimental results show that performance of cluster ensembles with the average linkage is superior to one with single linkage and complete linkage. Moreover, we also study the relationship between accuracy and ensemble size of the three methods.