| Texto completo | |
| Autor(es): |
Serapiao, Adriane B. S.
[1]
;
Correa, Guilherme S.
[1]
;
Goncalves, Felipe B.
[1]
;
Carvalho, Veronica O.
[1]
Número total de Autores: 4
|
| Afiliação do(s) autor(es): | [1] Univ Estadual Paulista, UNESP, IGCE, DEMAC, Sao Paulo - Brazil
Número total de Afiliações: 1
|
| Tipo de documento: | Artigo Científico |
| Fonte: | APPLIED SOFT COMPUTING; v. 41, p. 290-304, APR 2016. |
| Citações Web of Science: | 28 |
| Resumo | |
Data 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 SCAB, 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. (C) 2015 Elsevier By. All rights reserved. (AU) | |
| Processo FAPESP: | 13/23027-1 - Agrupamento de dados e inteligência coletiva com computação paralela em GPU |
| Beneficiário: | Adriane Beatriz de Souza Serapião |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/08730-8 - Desenvolvimento de técnicas de agrupamento de dados com algoritmos inspirados na natureza e implementação baseada em CUDA |
| Beneficiário: | Felipe Bonon Gonçalves |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |
| Processo FAPESP: | 13/08741-0 - Algoritmo Fish School Search em GPU para a tarefa de análise de agrupamentos de dados |
| Beneficiário: | Guilherme Sanchez Corrêa |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |