Modified K-Means Algorithm for Genetic Clustering

The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the K-means method , a new approach is developed, in which cluster centers are selected and computed appropriately. Examining the suggested approach by using standard data sets and comparing it with alternative methods in the literature reveals out that the proposed algorithm outperforms the K-means algorithm and other candidate algorithms in the pool.