摘要： 模糊C-均值聚类(FCM)应用广泛,但是它容易陷入局部最优,且对初始值很敏感。该文提出了一种基于遗传算法的模糊聚类方法,首先用遗传算法对模糊聚类中聚类中心的个数和聚类中心的选取进行指导,然后利用FCM进行聚类。实验结果表明:该方法可以在一定程度上避免FCM算法对初始值敏感和容易陷入局部最优解的缺陷,使聚类更合理,效果很好。

关键词: 遗传算法, 模糊C-均值聚类(FCM), 聚类分析

Abstract: The fuzzy C-means clustering algorithm (FCM) is applied extensively, but it can easily be trapped in a local optimum and also strongly depends on initialization. Therefore, a method of fuzzy clustering based on genetic algorithms is proposed in this paper. Genetic algorithms instruct to choose the number of cluster centers and the data that are cluster centers firstly, and clustering analysis is processed by FCM consequently. Experiment results show that the method can search global optimum partly so that the clustering analysis is more rational.

中图分类号: 

• TP18