HSS: Compact Set of Partitions via Hybrid Selection

Abstract

Inability to identify partitions of different sizes and shapes is a fundamental limitation of any clustering algorithm, especially when different regions ofthe search space contain clusters with varied characteristics. It is possible to apply diverse clustering algorithms, with different parameters, but then, it is necessary to deal with a large number of partitions. Techniques such as ensemble and multiobjective clustering treat this problem using distinct criteria, but they have high computational cost. Moreover, the ensemble technique generates a single solution, which may not represent every real partition present in the data. On the other hand, multiobjective clusteringmay generate a large number of partitions difficult to be analysed manually. In this paper, we propose a hybrid multiojective algorithm, HSS, that aims to return a reduced and yet diverse set of solutions. It can be divided in threesteps: (i) the application of a multiobjective algorithm to a set of base partitions for the generation of a Pareto Front (PF), (ii) the division of the solutions from the PF into a certain number of regions and (iii) the selectionof a solution per region, through the application of the Adjusted Rand Index. Experiments show the effectiveness of HSS in selecting a reduced number of partitions.