关键词: K-means, 进化算法, 变异算子, 全局分裂, 最大最小距离

Abstract: Evolutionary Algorithm (EA) can effectively overcome the drawback that Kmeans is sensitive to the initial clustering centers, thus enhancing the clustering performance. On the basis of evolutionary Kmeans clustering algorithm (FEAC), considering the randomness and locality in the splitting operator as a mutation operation, two improved splitting operators with global information (global splitting operator) were proposed. The idea of maxmin distance and the information of peripheral clusters were used to guide the selection of the initial splitting centers, in order to make splitting process more beneficial to global partition, furthermore, to improve the efficiency of the evolutionary clustering. The experimental results show that the improved algorithms based on global splitting operator outperform FEAC in terms of clusters number discovering and clustering accuracy.

Key words: Kmeans, Evolutionary Algorithm (EA), mutation operator, global splitting, maxmin distance