Evolutionary multiobjective clustering

Abstract

new approach to data clustering is proposed, in which two or more measures of cluster quality are simultaneously optimized using a multiobjective evolutionary algorithm (EA). For this purpose, the PESA-II EA is adapted for the clustering problem by the incorporation of specialized mutation and initialization procedures, described herein. Two conceptually orthogonal measures of cluster quality are selected for optimization, enabling, for the first time, a clustering algorithm to explore and improve different compromise solutions during the clustering process. Our results, on a diverse suite of 15 real and synthetic data sets - where the correct classes are known -demonstrate a clear advantage to the multiobjective approach: solutions in the discovered Pareto set are objectively better than those obtained when the same EA is applied to optimize just one measure. Moreover, the multiobjective EA exhibits a far more robust level of performance than both the classic k-means and average-link agglomerative clustering algorithms, outperforming them substantially on aggregate. © Springer-Verlag 2004