中文说明：模糊K-均值算法由K-均值算法派生而来。K-均值算法在聚类过程中，每次得到的结果虽然不一定是预期的结果，但类别之间的边界是明确的，聚类中心根据各类当前具有的样本进行修改。模糊K-均值算法在聚类过程中，每次得到的类别边界仍是模糊的，每类聚类中心的修改都需要用到所有样本，此外聚类准则也体现了模糊性。模糊K-均值算法聚类的结果仍是模糊集合，但是如果实际问题希望有一个明确的界限，也可以对结果进行去模糊化，通过一定的规则将模糊聚类转化为确定性分类。
English Description：
Fuzzy k-means algorithm derived from the k-means algorithm. In the course of k-means algorithm for clustering, the result may not be the expected result, but the boundaries of the category is clear, of cluster centers under the current modified sample. Fuzzy k-means algorithm for clustering in the process, each time you get the category boundaries are still vague, per category the modification requires all of these samples in addition clustering criteria reflect the ambiguity. Fuzzy k-means algorithm for clustering results are still fuzzy, but if real problems would like to have a clear set of limits, results can also be blurred, transformed fuzzy clustering through certain rules for classification of certainty.