Clustering is an unsupervised process that divides a given set of objects into groups so that objects within a cluster are highly similar with one another and dissimilar with the objects in other clusters. In this article, a new clustering method based on cat swarm optimization was proposed to find the proper clustering of data sets called K-means improvement and Simulated Annealing selection based cat swarm optimization clustering (KSACSOC). In the KSACSOC method, the seeking mode with k-means improvement was designed to enhance the clustering solution obtained in the process of iterations, and the tracing mode with simulated annealing selection was developed to explore the unvisited solution space. Experimental results on two artificial and six real life data sets are given to illustrate the superiority of the proposed algorithm over k-means algorithm, a simulated annealing clustering method, and a particle swarm optimization clustering method.
Key words: Clustering, cat swarm optimization, k-means, simulated annealing.
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