A constructive particle swarm algorithm for fuzzy clustering
Tipo
Artigo de evento
Data de publicação
2012
Periódico
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citações (Scopus)
3
Autores
Szabo A.
De Castro L.N.
Delgado M.R.
De Castro L.N.
Delgado M.R.
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Resumo
This paper proposes a fuzzy version of the crisp cPSC (Constructive Particle Swarm Clustering), called FcPSC (Fuzzy Constructive Particle Swarm Clustering). In addition to detecting fuzzy clusters, the proposed algorithm dynamically determines a suitable number of clusters in the datasets without the need of prior knowledge, necessary in cPSC to control the number of particles in the swarm. The FcPSC algorithm was applied to six databases from the literature and its performance was compared with that of Fuzzy C-Means, a Fuzzy Artificial Immune Network, a Fuzzy Particle Swarm Clustering and the crisp cPSC. FcPSC showed to be competitive with the algorithms used for comparison and the number of particles generated was smaller than for cPSC. © 2012 Springer-Verlag.
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Assuntos Scopus
Artificial Immune System , Bio-inspired algorithms , Data sets , Dynamic population , Fuzzy artificial immune networks , Fuzzy C mean , Fuzzy clusters , Fuzzy particle swarm , Fuzzy version , Number of clusters , Particle swarm , Particle swarm algorithm , Prior knowledge