An Optimized Artificial Bee Colony Algorithm for Clustering

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized artificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dynamic local optimization strategy utilizing roulette wheel selection algorithm for further enhancing local optimization. To prove its effectiveness, we validate the proposed algorithm on four datasets from UCI and compared the results with K-means, K-means++ and Artificial Bee Colony algorithm. Experiment results show that the proposed algorithm performs better than other clustering algorithms.