说明：  k-means算法是一种简单的迭代型聚类算法，采用距离作为相似性指标，从而发现给定数据集中的K个类，且每个类的中心是根据类中所有值的均值得到，每个类用聚类中心来描述。对于给定的一个包含n个d维数据点的数据集X以及要分得的类别K,选取欧式距离作为相似度指标，聚类目标是使得各类的聚类平方和最小
(K-means algorithm is a simple iterative clustering algorithm, which uses distance as a similarity index to find K classes in a given data set, and the center of each class is based on the mean of all the values in the class, and each class is described by the cluster center. For a given data set X containing N d-dimensional data points and the category K to be scored, the Euclidean distance is selected as the similarity index, and the clustering objective is to minimize the sum of squares of all kinds of clustering.)