A new multi-objective cluster ensemble based on modularity maximization
Mohammad Khodaparasti, Mohadeseh Ganji, Saeed Amirgholipour, Aboosaleh Mohammad Sharifi · Journal of Engineering Research · 2016
Conventional clustering algorithms utilize only one single criterion that may not conform to diverse shapes of the underlying clusters. But in this paper, we use two important criteria and propose a new multi-objective cluster ensemble model to empower finding clusters of different types. The first criterion 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 add further improvement, we also modify the Non-dominant sorting genetic algorithm (NSGAII) and propose a specialized crossover operator for it. Experimental results over thirteen UCI real data sets show that the proposed method outperforms other clustering methods.