A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

Hong Zhou; Ke Luo

doi:10.4028/www.scientific.net/AMM.325-326.1628

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326A K-Medoids Clustering Algorithm Based on Particle...

A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

Abstract:

Be aimed at the problems that K-medoids algorithm is easy to fall into the local optimal value and basic particle swarm algorithm is easy to fall into the premature convergence, this paper joins the Simulated Annealing (SA) thought and proposes a novel K-medoids clustering algorithm based on Particle swarm optimization algorithm with simulated annealing. The new algorithm combines the quick optimization ability of particle swarm optimization algorithm and the probability of jumping property with SA, and maintains the characteristics that particle swarm algorithm is easy to realize, and improves the ability of the algorithm from local extreme value point. The experimental results show that the algorithm enhances the convergence speed and accuracy of the algorithm, and the clustering effect is better than the original k-medoids algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1628-1631

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1628

Citation:

Cite this paper

Online since:

June 2013

Authors:

Hong Zhou, Ke Luo

Keywords:

K-Medoids Algorithm, Particle Swarm Algorithm, Simulated Annealing (SA)

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