An Automatic Data Clustering Algorithm Based on Differential Evolution

As one of the traditional optimization problems, clustering still plays a vital role for the researches both theoretically and practically nowadays. Although many successful clustering algorithms have been presented, most (if not all) need to be given the number of clusters before the clustering procedure is invoked. A novel differential evolution based clustering algorithm is presented in this paper to solve the problem of determining the number of clusters automatically. The proposed algorithm leverages the strengths of two technologies: one is a novel algorithm for finding the approximate number of clusters while the other is a heuristic search algorithm for automatic clustering. The experimental results show that the proposed algorithm can not only determine the approximate number of clusters automatically, but it can also provide more accurate results.