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International audience; The selection of the most appropriate clustering algorithm is not a straightforward task, given that there is no clustering algorithm capable of determining the actual groups present in any dataset. A potential solution is to use different clustering algorithms to produce a set of partitions (solutions) and then select the best partition produced according to a specified validation measure; these measures are generally biased toward one or more clustering algorithms. Nevertheless, in several real cases, it is important to have more than one solution as the output. To address these problems, we present a hybrid partition selection algorithm, HSS, which accepts as input a set of base partitions potentially generated from clustering algorithms with different biases and aims, to return a reduced and yet diverse set of partitions (solutions). HSS comprises three steps: (i) the application of a multiobjective algorithm to a set of base partitions to generate a Pareto Front (PF) approximation; (ii) the division of the solutions from the PF approximation into a certain number of regions; and (iii) the selection of a solution per region by applying the Adjusted Rand Index. We compare the results of our algorithm with those of another selection strategy, ASA. Furthermore, we test HSS as a post-processing tool for two clustering algorithms based on multiobjective evolutionary computing: MOCK and MOCLE. The experiments revealed the effectiveness of HSS in selecting a reduced number of partitions while maintaining their quality.

International audience; This paper presents a comparative study of cluster ensemble and multi-objective cluster ensemble algorithms. Our aim is to evaluate the extent to which such methods are able to identify the underlying structure hidden in a data set, given different levels of information they receive as input in the set of base partitions (BP). To do so, given a gold/reference partition, we produced nine sets of BP containing properties of interest for our analysis, such as large number of subdivisions of true clusters. We aim at answering questions such as: are the methods able to generate new and more robust partitions than those in the set of BP? are the techniques influenced by poor quality partitions presented in the set of BP?