中文说明：用matlab编写的k均值聚类程序，可直接运行调用-Prepared by the MATLAB K mean clustering procedures, can be directly run callK-均值聚类算法 1.初始化：选择c个代表点,...,,321cpppp 2.建立c个空间聚类表：CKKK...,21 3.按照最小距离法则逐个对样本X进行分类： ),(),,(min argJii Kxaddpxj 4.计算J及用各聚类列表计算聚类均值，并用来作为各聚类新的代表点（更新代表点） 5.若J不变或代表点未发生变化，则停止。否则转2.K-均值聚类算法 1.初始化：选择c个代表点,...,,321cpppp 2.建立c个空间聚类表：CKKK...,21 3.按照最小距离法则逐个对样本X进行分类： ),(),,(min argJii Kxaddpxj 4.计算J及用各聚类列表计算聚类均值，并用来作为各聚类新的代表点（更新代表点） 5.若J不变或代表点未发生变化，则停止。否则转2.K-均值聚类算法 1.初始化：选择c个代表点,...,,321cpppp 2.建立c个空间聚类表：CKKK...,21 3.按照最小距离法则逐个对样本X进行分类： ),(),,(min argJii Kxaddpxj 4.计算J及用各聚类列表计算聚类均值，并用来作为各聚类新的代表点（更新代表点） 5.若J不变或代表点未发生变化，则停止。否则转2.K-均值聚类算法 1.初始化：选择c个代表点,...,,321cpppp 2.建立c个空间聚类表：CKKK...,21 3.按照最小距离法则逐个对样本X进行分类： ),(),,(min argJii Kxaddpxj 4.计算J及用各聚类列表计算聚类均值，并用来作为各聚类新的代表点（更新代表点） 5.若J不变或代表点未发生变化，则停止。否则转2.K-均值聚类算法 1.初始化：选择c个代表点,...,,321cppp
English Description：
The K-means clustering program written in Matlab can directly run and call - prepared by the MATLAB K-means clustering procedures, can be directly run call - means clustering algorithm 1. Initialization: select c representative points,..., 321cpppp 2. Establish C spatial clustering tables: ckkk..., 21 & nbsp; 3. Classify sample x one by one according to the minimum distance rule: & nbsp;), (),, (min argji kxaddpxj & nbsp; 4. Calculate J and calculate the cluster mean value with each cluster list, and use it as the new representative point of each cluster (update representative point) & nbsp; 5. If J does not change or the representative point does not change, stop. Otherwise, turn to 2. K-means clustering algorithm & nbsp; 1. Initialization: select c representative points,..., 321cpppp 2. Establish C spatial clustering tables: ckkk..., 21 & nbsp; 3. Classify sample x one by one according to the minimum distance rule: & nbsp;, (), (min argji kxaddpxj & nbsp; 4. Calculate J and use each clustering list to calculate the clustering mean, and use it as the new representative points of each cluster (update representative points) &If J does not change or the representative point does not change, stop. Otherwise, turn to 2. K-means clustering algorithm & nbsp; 1. Initialization: select c representative points,..., 321cpppp 2. Establish C spatial clustering tables: ckkk..., 21 & nbsp; 3. Classify sample x one by one according to the minimum distance rule: & nbsp;, (), (min argji kxaddpxj & nbsp; 4. Calculate J and use each clustering list to calculate the clustering mean, and use it as the new representative points of each cluster (update representative points) &If J does not change or the representative point does not change, stop. Otherwise, turn to 2. K-means clustering algorithm & nbsp; 1. Initialization: select c representative points,..., 321cpppp 2. Establish C spatial clustering tables: ckkk..., 21 & nbsp; 3. Classify sample x one by one according to the minimum distance rule: & nbsp;, (), (min argji kxaddpxj & nbsp; 4. Calculate J and use each clustering list to calculate the clustering mean, and use it as the new representative points of each cluster (update representative points) &If J does not change or the representative point does not change, stop. Initialization: select c representative points,..., 321cppp