Clustering based fuzzy particle swarm optimization

Alizadeh, Meysam; Fotoohi, Elnaz; Roshanaei, Vahid; Safavieh, Ehsan

doi:10.1109/nafips.2009.5156487

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…The Fuzzy C-Means algorithm (FCM), proposed by Bezdek [7], is one of the most well-known fuzzy clustering algorithms in the literature [20]. It partitions objects into groups by varying the degree of membership of the objects to be grouped [18] to the groups determined.…”

Section: A Fuzzy C-meansmentioning

confidence: 99%

The proposal of a fuzzy clustering algorithm based on particle swarm

Szabó

Castro

Delgado

2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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This paper proposes the Fuzzy Particle Swarm Clustering (FPSC) algorithm, which is an extension of the crisp data clustering algorithm PSC particularly tailored to deal with fuzzy clusters. The main structural changes of the original PSC algorithm to design FPSC occurred in the selection and evaluation steps of the winner particle, comparing the degree of membership of each object from the database in relation to the particles in the swarm. The FPSC algorithm was applied to eight databases from the literature with the purpose of benchmarking and its performance was compared with that of Fuzzy C-Means and Fuzzy PSO. The results showed that the FPSC algorithm is competitive with the algorithms discussed in this paper.

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Section: A Fuzzy C-meansmentioning

confidence: 99%