The behavior of particles in the Particle Swarm Clustering algorithm

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dc.contributor.authorSzabo A.
dc.contributor.authorPrior A.K.F.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T01:30:38Z
dc.date.available2024-03-13T01:30:38Z
dc.date.issued2010
dc.description.abstractThe Particle Swarm Clustering (PSC) algorithm uses collective intelligence to solve clustering problems. It simulates the interaction of individuals, which use their own experience (cognitive term), social experience (social term) and interaction with the environment (self-organizing term) to cluster objects in different groups. In this work a study of the behavior of particles and an analysis of the PSC convergence were performed considering each term that composes the particles' adaptation equation. The objective was to evaluate the relevance of each of these terms within the context of clustering data. © 2010 IEEE.
dc.identifier.doi10.1109/FUZZY.2010.5584118
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37135
dc.relation.ispartof2010 IEEE World Congress on Computational Intelligence, WCCI 2010
dc.rightsAcesso Restrito
dc.titleThe behavior of particles in the Particle Swarm Clustering algorithm
dc.typeArtigo de evento
local.scopus.citations4
local.scopus.eid2-s2.0-78549293069
local.scopus.subjectClustering data
local.scopus.subjectClustering problems
local.scopus.subjectCollective intelligences
local.scopus.subjectParticle swarm
local.scopus.subjectSelf organizing
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78549293069&origin=inward
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