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New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. © 2011 Springer-Verlag.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@6b81d52c ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@18d272d8 Through org.apache.xalan.xsltc.dom.DOMAdapter@2071cfa3; Conference Code:86007
R. Karthi, Rajendran, Cb, and Rameshkumar, K., “Neighborhood search assisted particle swarm optimization (NPSO) algorithm for partitional data clustering problems”, Communications in Computer and Information Science, vol. 192 CCIS, pp. 552-561, 2011.