Hybrid strategy for selecting compact set of clustering partitions

Autor(es)

Antunes, Vanessa ^[1] ; Sakata, Tiemi C. ^[1] ; Faceli, Katti ^[1] ; de Souto, Marcilio C. P. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Campus Sorocaba Rodovia Joao Leme dos Santos, BR-18052780 Sorocaba, SP - Brazil

^[2] Univ Orleans, LIFO EA 4022, Orleans - France

• Tipo de documento: Artigo Científico
• Fonte: APPLIED SOFT COMPUTING; v. 87, FEB 2020.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 15/21560-0 - Ferramenta de visualização interativa para exploração e aprendizado a partir de conjuntos de soluções de agrupamento
• Beneficiário: Katti Faceli
• Modalidade de apoio: Bolsas no Exterior - Pesquisa