k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means algorithm is as follows: First, choose k objects from n data objects as the initial cluster centers　while for the rest of the other objects, according to the similarity (distance) with those of their cluster centers, They were assigned to the most similar (represented by the cluster center) clustering　then calculated for each cluster received new cluster center (the cluster mean all objects)　repeats this process Until the beginning of a standard measure function convergence. MSE is generally used as the standard measure function k clustering has the following characteristics: each cluster itself as compact as possible, and to separate between the clusters as possible. Here is what I wrote the source code.