Publication Type:

Book Chapter

Source:

Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) , Springer-Verlag, Berlin, Heidel berg, Volume 236, p.75 - 88 (2009)

URL:

https://link.springer.com/chapter/10.1007/978-3-642-03211-0_7

Abstract:

In this paper, a novel Discrete Particle Swarm Optimization Algorithm (DPSOA) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form. The DPSOA algorithm uses of a simple probability approach to construct the velocity of particle followed by a search scheme to constructs the clustering solution. DPSOA algorithm has been applied to solve the data clustering problems by considering two performance metrics, such as TRace Within criteria (TRW) and Variance Ratio Criteria (VRC). The results obtained by the proposed algorithm have been compared with the published results of Basic PSO (B-PSO) algorithm, Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Combinatorial Particle Swarm Optimization (CPSO) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.

Cite this Research Publication

R. Karthi, Arumugam, S., and Rameshkumar, K., “Discrete Particle Swarm Optimization Algorithm for Data Clustering”, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) , Springer-Verlag, Berlin, Heidel berg, vol. 236, 2009, pp. 75 - 88.

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