Combining IWC and PSO to Enhance Data Clustering

Ahmed Z. Skaik, Wesam M. Ashour


In this paper we propose a clustering method based on combination of the Particle Swarm Optimization (PSO) and the inverse weighted clustering algorithm IWC, It is shown how PSO can be used to find the centroids of a user specified number of clusters and basically uses PSO to refine the clusters formed by IWC. Since PSO algorithm was showed to successfully converge during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, IWC algorithm can achieve faster convergence to optimum solution, Experimental results show that the proposed technique has much potential to improve the clustering process.


data clustering, particle swarm optimization, inverse weighted K-Means

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