基于群进化策略的模糊聚类算法

摘要

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.%针对传统的聚类算法需要知道类的真实数目,以及容易陷入局部最优的缺陷,提出基于群进化策略的模糊聚类算法,简称fuzzyGAC.该算法将群进化策略与模糊聚类结合起来,通过两个阶段(继承阶段和重新分配阶段)来产生新的聚类结果.将提出的算法与模糊C均值算法、差分算法、粒子群算法进行比较,实验结果表明,就类的数目和聚类中心而言,该算法可以自适应地修正类的数目并且提供最优的聚类中心.