Evaluation of Relative Indexes for Multi-objective Clustering

One of the biggest challenges in clustering is finding a robust and versatile criterion to evaluate the quality of clustering results. In this paper, we investigate the extent to which unsupervised criteria can be used to obtain clusters highly correlated to external labels. We show that the usefulness of these criteria is data-dependent and for most data sets multiple criteria are required in order to identify the best performing clustering algorithm. We present a multi-objective evolutionary clustering algorithm capable of finding a set of high-quality solutions. For the real world data sets examined the Pareto front can offer better clusterings than simply optimizing a single unsupervised criterion.