中文说明：应用背景 k均值聚类是最著名的划分聚类算法，由于简洁和效率使得他成为所有聚类算法中最广泛使用的。给定一个数据点集合和需要的聚类数目k，k由用户指定，k均值算法根据某个距离函数反复把数据分入k个聚类中。 关键技术 先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。这个过程将不断重复直到满足某个终止条件。终止条件可以是以下任何一个： 1)没有（或最小数目）对象被重新分配给不同的聚类。 2)没有（或最小数目）聚类中心再发生变化。 3)误差平方和局部最小。
English Description：
Application backgroundK means clustering is the most famous clustering algorithm, because of the simplicity and efficiency, he has become the most widely used in all clustering algorithms.. Given a collection of data points and the number of clustering K, K by the user specified, K mean algorithm based on a distance function repeatedly into k clustering of data.Key TechnologyThe K object is randomly selected as the initial clustering center.. Then calculate the distance between each object and each seed cluster center, assign each object to its nearest clustering center.. The cluster centers and the objects assigned to them represent a clustering. Once all the objects are assigned, each clustering center will be re computed according to the existing object of the clustering.. This process will continue to repeat until a certain termination condition is met. The termination condition can be any of the following:1) no (or minimum number of) obje