用Matlab实现基于核函数的C均值聚类图像分割，实验好，好用-Using Matlab implementation of kernel-based C-means clustering image segmentation, experimental is good, easy to use

Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysis (kernel ECA) as a new method

基于核的FCM，可以用于聚类或者图像分割，但运行速度不是很快 自己不知道如何优化-Kernel-based FCM, can be used for clustering or image segmentation, but the speed is not fast they do not know how to optimize

模糊核聚类算法的几篇文章还有其matlab的几个程序-Fuzzy kernel clustering algorithm several articles Matlab program

模糊核聚类图像分割，有很好的实例和码源，值得大家学习。-Fuzzy kernel clustering image segmentation, a very good examples and source code, worth learning.