Far East Journal of Electronics and Communications
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Abstract: Clustering is an
important unsupervised classification technique. When used on a set of objects,
it helps to identify some inherent structures present in the objects by
classifying them into subsets that have some meaning in the context of a
particular problem. In this paper, we present the Particle Swarm Optimization (PSO)
clustering algorithm. It performs a globalized search in the entire solution
space. The proposed PSOtechnique is compared with the PSO algorithm presented by Merwe et al.
[8]. The two algorithms are applied to an artificial data, the
Wisconsin
breast cancer database and Iris plants database. The results show that the
proposed PSO convergence is faster to lower quantization error.
Keywords and phrases: particle swarm optimization, data clustering.
