Abstract:To deal with the problem of the sensitivity of initialization and premature convergence, this paper proposes a novel K-means optimization clustering algorithm based on particle swarm optimization and multi-groups merging, namely M-PSO-Means. Firstly the algorithm selects the initial cluster center by improving particle swarms clustering algorithm under default number of clustering, then optimizes the clustering, and last carries out cluster merging based on multi-groups merging condition to obtain the best clustering results. The experimental results show that, the algorithm can effectively solve the defects of K-means algorithm, and has a faster convergence rate and better global search ability, as well as better cluster category effect.