Evolutionary Multiobjective Clustering

Clustering is a core problem in data-mining with innumerable applications spanning many fields. A key difficulty of effective clustering is that for unlabelled data a ‘good’ solution is a somewhat ill-defined concept, and hence a plethora of valid measures of cluster quality have been devised. Most clustering algorithms optimize just one such objective (often implicitly) and are thus limited in their scope of application. In this paper, we investigate whether an EA optimizing a number of different clustering quality measures simultaneously can find better solutions. Using problems where the correct classes are known, our results show a clear advantage to the multiobjective approach: it exhibits a far more robust level of performance than the classic k-means and average-link agglomerative clustering algorithms over a diverse suite of 15 real and synthetic data sets, sometimes outperforming them substantially.