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Neighborhood Search Assisted Particle Swarm Optimization (NPSO) Algorithm for Partitional Data Clustering Problems
, R. Karthi, Rameshkumar K.
Published in Springer Berlin Heidelberg
Pages: 552 - 561

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.

About the journal
JournalData powered by TypesetCommunications in Computer and Information Science
PublisherData powered by TypesetSpringer Berlin Heidelberg
Open AccessNo