模糊C-均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。由于原始的模糊C-均值聚类算法没有考虑图像的空间信息，算法对图像中的噪音点十分敏感。针对这个问题，很多稳健模糊C-均值聚类算法被提出。通常的做法是在原来模糊C-均值聚类的目标函数中加入空间信息惩罚项。本文讨论这类方法，具体分析不同算法的空间信息加入方式，并指出其优缺点。

Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation. Without considering the spatial information of images, the original fuzzy C-means algorithm is very sensitive to image noise. Lots of robust fuzzy C-means algorithms have been proposed in the literature to solve this problem. A general solution is to add the spatial information to the object function of fuzzy C-means. This paper describes the way of embedding the spatial information and shows the advantages and disadvantages of this method.