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A Reproductive Particle Swarm Optimization Algorithm for Data Clustering

Mingru Zhao, Hengliang Tang, Jian Guo, Yuan Sun


Data clustering is a popular approach for automatically finding classes or groups of patterns. In recent years, data clustering is still a popular analysis tool for data statistics to identify some inherent structures that presents in the objects. In this paper, in order to improve the convergence and global searching capacity of particle swarm optimization(PSO) in solving data clustering ,an improved particle swarm optimization clustering algorithm (EPSOK) based on reproductive strategy is presented. In the algorithm, the best particles in the search process reproduce, at the same time, the worst particles disappear. Through comparing with the classical K-Means algorithm, the improved algorithm has obvious advantages in the experiments.


particle swarm optimization, K-Means, clustering, reproductive strategy.

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