软硬结合的快速模糊C-均值聚类算法的研究

讨论的是对模糊C-均值聚类方法的改进,在原有的模糊C-均值算法的基础上,提出一种软硬结合的快速模糊C-均值聚类算法。快速模糊C-均值聚类算法是在模糊C-均值聚类算法之前加入一层硬C-均值聚类算法。硬聚类算法能比模糊聚类算法以高得多的速度完成,将硬聚类中心作为模糊聚类中心的迭代初值,从而提高模糊C-均值聚类算法的收敛速度,这对于大量数据的聚类是很有意义的。用数据仿真验证了这种快速模糊C-均值聚类算法比模糊C-均值算法迭代调整过程短,收敛速度快,聚类效果好。

Research of fast fuzzy C-means clustering algorithm based on soft and hard clustering

This paper discusses how to improve the fuzzy C-means clustering algorithm(FCM).On the basis of FCM,the paper puts forward a kind of fast fuzzy C-means clustering algorithm based on soft and hard clustering.The fast FCM inserts one layer of hard C-means clustering algorithm in front of FCM.The hard C-means clustering algorithm can be finished at much higher speed than FCM.In order to improve the convergence speed of FCM,the authors regard the cluster centers of hard C-means clustering algorithm as the initial value of fuzzy cluster centers.It is very meaningful for a large number of data clustering.In addition,this paper proves that the fast fuzzy C-means clustering algorithm has a shorter adjusting iteration course and a faster convergence speed than FCM and the clustering result achieved from data emulation is very ideal.