Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. The term “clustering” is used in several research communities to describe methods for grouping of unlabeled data. These communities have different terminologies and assumptions for the components of the clustering process and the context in which clustering is used. This paper looks into the use of Particle Swarm Optimization (PSO) for cluster analysis. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. In this paper a modification strategy is proposed for the particle swarm optimization (PSO) algorithm and applied in the data sets. This paper provides a method for particles to steer clear off from local stagnation and the local search is applied to improve the goodness of fitting. The effectiveness of this concept is demonstrated by cluster analysis. Results show that the model provides enhanced performance and maintains more diversity in the swarm and thereby allows the particles to be robust to trace the changing environment.
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