Dominant Sets as a Framework for Cluster Ensembles: An Evolutionary Game Theory Approach

Abstract

Ensemble clustering aggregates partitions obtained from several individual clustering algorithms. This can improve the accuracy of results from individual methods and provide robustness against variability in the methods applied. Theorems show one can find dominant sets (clusters) very efficiently by using an evolutionary game theoretic approach. Experiments on an MRI data set consisting of about 4 million data are detailed. The distributed dominant set framework generates partitions of quality slightly better than clustering all the data using fuzzy C means.