kmeans算法思想:

1.从数据集中随机选取k个初始点作为质心。

2.遍历数据集中所有的点，求出每个点到每个质心的距离，找出距离改点最近的质心，并改变此点类型为此质点的类型。

3.重新为每个类别更新其质心。

4.重复2,3,步直到最后两次质心位置相同退出while循环。

补充用python实现的代码，要给python装numpy和matplotlib库，建议直接装anaconda，装好了anaconda默认安装了spyder，里面集成了这两个库，比较方便。

建立kmeans.py文件，编写如下代码:

# -*- coding: utf-8 -*-from numpy import *import matplotlib.pyplot as plt#计算两向量之间的欧式距离，在这里是计算两点之间的距离def euclDistance(vector1,vector2):    return sqrt(sum(power(vector2-vector1,2)))#初始化......#从原始数据中产生随机的k个数据存入centroidsdef initCentroids(dataSet,k):    numSamples,dim=dataSet.shape#返回dataSet的行和列    centroids=zeros((k,dim))#创建k行dim列的矩阵    for i in range(k):        index=int(random.uniform(0,numSamples))#从0到numSamples中随机产生一个数        centroids[i,:]=dataSet[index,:]    return centroidsdef kmeans(dataSet,k):#此算法用到3个数据集，dataSet:n行两列表示原始数据，clusterAssment:n行两列，第一列表示                      #原始数据的类型，第二列表示此点到质心的距离，centriods:k行两列表示点群的质心    numSamples=dataSet.shape[0]    clusterAssment=mat(zeros((numSamples,2)))#clusterAssment中存放点聚类的类别以及与该类别质心的距离    clusterChanged=True    centroids=initCentroids(dataSet,k)#从原始数据中产生随机的k个数据存入centroids，代表k个质心    while clusterChanged:        clusterChanged=False        for i in xrange(numSamples):            minDist=100000.0            minIndex=0            for j in range(k):#从k个质心中选取距离i行这个点最小的一个质心                distance=euclDistance(centroids[j,:],dataSet[i,:])                if distance
     
      len(mark):        print "Sorry! Your k is too large! please contact Zouxy"        return 1    #画聚类后的图      for i in xrange(numSamples):        markIndex=int(clusterAssment[i,0])        plt.plot(dataSet[i,0],dataSet[i,1],mark[markIndex],markersize=6)    mark=['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '
     

 本程序所用到的数据集为80个点，可以复制到自己的test.txt文档中与此程序放在同一目录下:

1.658985,4.285136      -3.453687,3.424321      4.838138,-1.151539      -5.379713,-3.362104      0.972564,2.924086      -3.567919,1.531611      0.450614,-3.302219      -3.487105,-1.724432      2.668759,1.594842      -3.156485,3.191137      3.165506,-3.999838      -2.786837,-3.099354      4.208187,2.984927      -2.123337,2.943366      0.704199,-0.479481      -0.392370,-3.963704      2.831667,1.574018      -0.790153,3.343144      2.943496,-3.357075      -3.195883,-2.283926      2.336445,2.875106      -1.786345,2.554248      2.190101,-1.906020      -3.403367,-2.778288      1.778124,3.880832      -1.688346,2.230267      2.592976,-2.054368      -4.007257,-3.207066      2.257734,3.387564      -2.679011,0.785119      0.939512,-4.023563      -3.674424,-2.261084      2.046259,2.735279      -3.189470,1.780269      4.372646,-0.822248      -2.579316,-3.497576      1.889034,5.190400      -0.798747,2.185588      2.836520,-2.658556      -3.837877,-3.253815      2.096701,3.886007      -2.709034,2.923887      3.367037,-3.184789      -2.121479,-4.232586      2.329546,3.179764      -3.284816,3.273099      3.091414,-3.815232      -3.762093,-2.432191      3.542056,2.778832      -1.736822,4.241041      2.127073,-2.983680      -4.323818,-3.938116      3.792121,5.135768      -4.786473,3.358547      2.624081,-3.260715      -4.009299,-2.978115      2.493525,1.963710      -2.513661,2.642162      1.864375,-3.176309      -3.171184,-3.572452      2.894220,2.489128      -2.562539,2.884438      3.491078,-3.947487      -2.565729,-2.012114      3.332948,3.983102      -1.616805,3.573188      2.280615,-2.559444      -2.651229,-3.103198      2.321395,3.154987      -1.685703,2.939697      3.031012,-3.620252      -4.599622,-2.185829      4.196223,1.126677      -2.133863,3.093686      4.668892,-2.562705      -2.793241,-2.149706      2.884105,3.043438      -2.967647,2.848696      4.479332,-1.764772      -4.905566,-2.911070

 测试文件a.py:

# -*- coding: utf-8 -*-"""Created on Sun Mar  5 12:30:11 2017@author: chao"""from numpy import *  import kmeans   ## 读数据print "step 1: load data..."  dataSet = []  fileIn = open('/home/chao/Desktop/python_work/kmeans/test.txt')  for line in fileIn.readlines():      lineArr = line.strip().split(',')      dataSet.append([float(lineArr[0]), float(lineArr[1])]) #将每一组数据读入列表里面   ## 聚类print "step 2: clustering..."  dataSet = mat(dataSet) #mat函数创建矩阵k = 4centroids, clusterAssment = kmeans.kmeans(dataSet, k)  ## 画出结果图print "step 3: show the result..."  kmeans.showCluster(dataSet, k, centroids, clusterAssment)

 运行结果图:

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