Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis

Improvement in the quality of cluster centers and minimization of intra-cluster distance are two most challenging areas of K-means clustering algorithm. Due to predetermined number of clusters, it is difficult to predict the exact value of k. Furthermore, in case of non-globular clusters, K-means fails to get optimal cluster center in a data set. In this paper, a hybrid improved particle swarm optimization-based evolutionary K-means clustering method has been proposed to obtain the optimal cluster center. The hybridization of improved PSO and genetic algorithm (GA) along with K-means algorithm improves the convergence speed as well as helps to find the global optimal solution. In the first stage, IPSO has been used to get a global solution in order to get optimal cluster centers. Then, the crossover steps of GA are used to improve the quality of particles and mutation is used for diversification of solution space in order to avoid premature convergence. The performance analysis of the proposed method is compared with other existing clustering techniques like K-means, GA-K-means, and PSO-K-means.

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