K-means算法，算法步骤如下：

Step1.利用式(2)计算距离矩阵D=()，其中=dist[i, j] ()；

Step2.扫描坐标距离矩阵D，寻找距离的最大值和最小值，用式(3)计算limit；

Step3.扫描坐标距离矩阵D，寻找矩阵中距离最小的2个数据a，b，将数据a，b加入集合，={a,b}，同时将数据a，b从U中删除，更新距离矩阵D；

Step4.利用 (4)式在U中寻找距离集合最近的数据样本t，如果小于limit，则将t加入集合，同时将t从集合U中删除，更新距离矩阵D，重复Step5，否则停止；

Step5.若i<k，i=i+1，重复步骤Step3、Step4，直至k个集合完成；

Step6.取集合中数据的算术平均值记作数据中心，并计算得到的坐标值，完成k个数据中心的选取。-Algorithm steps are as follows:

Step1. Type (2) is used to calculate the distance matrix D = (), including = dist [I, j] ()

Step2. Scan coordinate distance matrix D, looking for the maximum and the minimum distance, use type (3) calculate the limit

Step3. Scan coordinate distance matrix D, looking for matrix minimum distance of two data a, b, and the data to a, b to join the collection, = {a, b}, at the same time the data a, b is removed from the U, update the distance matrix D

Step4. Using (4) in the U find closest to the collection of data samples t, if less than the limit, then t join collection, at the same time t is removed from the set U, update the distance matrix D, repeat Step5, otherwise stop

Step5. If I < k, I = I+ 1, repeat steps Step3, Step4, until k collection is complete

Step6. Take the arithmetic mean of the collection of data for the data center, and to calculate the coordinates, to complete the selection of k data center.

The above steps distribution cu