Abstract: Data clustering plays a very important role in intelligent information processing, but the traditional K-means algorithm is sensitive to initial clustering centers. With the development of intelligent optimization algorithm, people uses intelligent optimization algorithm to cluster data and achieve a certain effect, but it is still easy to fall into the local optimization. In this paper, the competitive swarm optimizer algorithm which has achieved good results in high dimensional optimization problem is exploited for data clustering, the powerful exploration ability of competitive swarm optimizer is used to search clustering centers for data clustering. The experimental results on the five data sets of UCI show that the competitive swarm optimizer can not only get better clustering effect but also better convergence performance than genetic algorithm and particle swarm optimization algorithm.

Key words: clustering, competitive swarm optimizer, UCI dataset