A bee-inspired multiobjective optimization clustering algorithm
Tipo
Artigo de evento
Data de publicação
2016
Periódico
Proceedings of the 15th IASTED International Conference on Intelligent Systems and Control, ISC 2016
Citações (Scopus)
0
Autores
Cruz D.
Politi A.
Cunha D.
De Castro L.N.
Maia R.D.
Politi A.
Cunha D.
De Castro L.N.
Maia R.D.
Orientador
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ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
Multiobjective clustering techniques have been used to simul-taneously consider several complementary aspects of cluster-ing quality. They optimize more than one cluster validity index simultaneously, leading to high-quality results, and have emerged as attractive and robust alternatives for clustering problems. This paper proposes a bee-inspired multiobjective optimization algorithm to solve data clustering problems. The algorithm was run for different datasets and the results ob-tained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically deter-mined.
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Assuntos Scopus
Bee-inspired algorithms , Cluster validity indices , Clustering , Clustering problems , Diversity of solutions , Multi objective , Multi-objective clustering , Number of clusters