dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:00:28Z
dc.date.available2018-12-11T17:00:28Z
dc.date.created2018-12-11T17:00:28Z
dc.date.issued2016-04-01
dc.identifierApplied Soft Computing Journal, v. 41, p. 290-304.
dc.identifier1568-4946
dc.identifierhttp://hdl.handle.net/11449/172465
dc.identifier10.1016/j.asoc.2015.12.032
dc.identifier2-s2.0-84955571121
dc.identifier2-s2.0-84955571121.pdf
dc.identifier6997814343189860
dc.identifier0000-0001-9728-7092
dc.description.abstractData clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K-Means and on K-Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS). The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS-SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.
dc.languageeng
dc.relationApplied Soft Computing Journal
dc.relation1,199
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectData clustering
dc.subjectFish School Search
dc.subjectGraphics processing units
dc.subjectK-Harmonic Means
dc.subjectK-Means
dc.subjectParticle Swarm Optimization
dc.titleCombining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units
dc.typeArtículos de revistas


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