关键词: 模糊聚类, 群进化策略, 模糊C均值算法

Abstract: Due to conventional clustering algorithms which should get the true number of clusters and easily fall into local optimum, a new algorithm is proposed, called fuzzyGAC for shortly. The algorithm combines group evolution strategy with fuzzy clustering, and uses two phases（i.e., inheritance phase and redistribution phase） to generate a new clustering solution. The proposed approach is compared with fuzzy C-means algorithm, Particle Swarm Optimization（PSO） algorithm, and Differential Evolution（DE）. Moreover, the results indicate that fuzzyGAC can provide acceptable results in terms of both determining the correct number of clusters and the accurate cluster centroids.

Key words: fuzzy clustering, grouping evolution strategy, fuzzy C-means algorithm