A constructive particle swarm algorithm for fuzzy clustering

Authors:
Alexandre Szabo;Leandro Nunes de Castro;Myriam Regattieri Delgado
Affiliations:
Natural Computing Laboratory, Mackenzie University, Séo Paulo, Brazil;Natural Computing Laboratory, Mackenzie University, Séo Paulo, Brazil;Federal University of Technology of Paraná, Curitiba, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

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.