A new multi-objective cluster ensemble based on modularity maximization

Abstract

Conventional clustering algorithms utilize only one single criterion that may not    conform to diverse shapes of the underlying clusters. But in this paper, we hire two important criteria and propose a new multi-objective cluster ensemble model to empower finding clusters of different types. The first criteria is the well-known sum of squared error. The second criterion is modularity which is originally a measure of evaluating communities in social networks. We maximize modularity as a consensus function of cluster ensemble. In order to further improvement, we also modify Non Dominant Sorting Genetic Algorithm (NSGAII) and propose a specialized crossover operator for it. Experimental results over seven UCI real data sets show that the proposed method significantly outperforms other clustering methods.