中文说明：k-means 算法接受参数 k ；然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报
English Description：
K-means algorithm accepts parameters k; prior input of n data objects are then divided into k clusters in order to make the access to the cluster to meet: high similarity of the object in the same cluster and less similarity between different objects in the cluster. Cluster similarity is the use of the cluster object is obtained by means of a "Center" (Center of gravity) is calculated. K-means algorithm is the most classical clustering method based on Division, is one of the top ten algorithms of data mining. The basic idea of k-means algorithm is: k points in space-centric clustering on objects closest to their classification. Through an iterative approach, successive values in cluster centers updated until you get the best cluster results.