Clustering Ensemble: A Multiobjective Genetic Algorithm based Approach

Abstract

Abstract Clustering ensemble refers to the problem of obtaining a final clustering of some data set from a set of input clustering solutions. In this article, the clustering ensemble problem has been modeled as a multiobjective optimization problem and a multiobjective evolutionary algorithm has been used for this purpose. The proposed multiobjective evolutionary clustering ensemble algorithm (MOECEA) evolves a clustering solution from the input clusterings by optimizing two criteria simultaneously. The first objective is to maximize the similarity of the resultant clustering with all the input clusterings, where the similarity between two clustering solutions is computed using adjusted Rand index. The second criteria is to minimize the standard deviation among the similarity scores in order to prevent the evolved clustering solution to be very similar with one of the input clusterings and very dissimilar with the others. The performance of the proposed algorithm has been compared with that of other well-known existing cluster ensemble algorithms for a number of artificial and real-life data sets.