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Clustering data are one of the key issues in data mining that has attracted much attention. One of the famous algorithms in this field is K-Means clustering that has been successfully applied to many problems. But this method has its own disadvantages, such as the dependence of the efficiency of this method to initialization of cluster centers. To improve the quality of K-Means, hybridization of this algorithm with other methods suggested by many researchers. Particle Swarm Optimization (PSO) is one of Swarm Intelligence (SI) algorithms that has been combined with K-Means in various ways. In this paper, we suggest another way of combining K-Means and PSO, using the strength of both algorithms. Most of the methods introduced in the context of clustering, that hybridized K-Means and PSO, used them sequentially, but in this paper we applied them intertwined. The results of the investigation of this algorithm, on the number of benchmark databases from UCI Machine Learning Repository, reflect the ability of this approach in clustering analysis.

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