Publication Type:

Journal Article


Communications in Computer and Information Science, Volume 192 CCIS, Number PART 3, Kochi, p.552-561 (2011)





biology, cluster analysis, Clustering algorithms, Data clustering, Genetic algorithms, Marriott Criteria, Particle swarm, Particle swarm optimization (PSO), Trace Within criteria, Variance ratio


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.


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Cite this Research Publication

R. Karthi, Rajendran, Cb, and K. Ramesh Kumar, “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.