说明：  K均值聚类算法是先随机选取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。终止条件可以是没有（或最小数目）对象被重新分配给不同的聚类，没有（或最小数目）聚类中心再发生变化，误差平方和局部最小。
(K-means clustering algorithm first randomly selects K objects as the initial clustering center. Then the distance between each object and each seed cluster center is calculated, and each object is assigned to the nearest cluster center. Cluster centers and objects assigned to them represent a cluster. Once all the objects are allocated, the cluster center of each cluster is recalculated according to the existing objects in the cluster. This process will be repeated until a termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers change again, and the sum of squares of errors is local minimum.)