python中kmeans聚类实现代码

k-means算法思想较简单，说的通俗易懂点就是物以类聚，花了一点时间在python中实现k-means算法，k-means算法有本身的缺点，比如说k初始位置的选择，针对这个有不少人提出k-means++算法进行改进；另外一种是要对k大小的选择也没有很完善的理论，针对这个比较经典的理论是轮廓系数，二分聚类的算法确定k的大小，在最后还写了二分聚类算法的实现，代码主要参考机器学习实战那本书：

#encoding:utf-8
'''''
Created on 2015年9月21日
@author: ZHOUMEIXU204
''' 

path=u"D:\\Users\\zhoumeixu204\\Desktop\\python语言机器学习\\机器学习实战代码  python\\机器学习实战代码\\machinelearninginaction\\Ch10\\"
import numpy as np
def loadDataSet(fileName): #读取数据
  dataMat=[]
  fr=open(fileName)
  for line in fr.readlines():
    curLine=line.strip().split('\t')
    fltLine=map(float,curLine)
    dataMat.append(fltLine)
  return dataMat
def distEclud(vecA,vecB):  #计算距离
  return np.sqrt(np.sum(np.power(vecA-vecB,2)))
def randCent(dataSet,k):   #构建镞质心
  n=np.shape(dataSet)[1]
  centroids=np.mat(np.zeros((k,n)))
  for j in range(n):
    minJ=np.min(dataSet[:,j])
    rangeJ=float(np.max(dataSet[:,j])-minJ)
    centroids[:,j]=minJ+rangeJ*np.random.rand(k,1)
  return centroids
dataMat=np.mat(loadDataSet(path+'testSet.txt'))
print(dataMat[:,0]) 

# 所有数都比-inf大
# 所有数都比+inf小
def kMeans(dataSet,k,distMeas=distEclud,createCent=randCent):
  m=np.shape(dataSet)[0]
  clusterAssment=np.mat(np.zeros((m,2)))
  centroids=createCent(dataSet,k)
  clusterChanged=True
  while clusterChanged:
    clusterChanged=False
    for i in range(m):
      minDist=np.inf;minIndex=-1 #np.inf表示无穷大
      for j in range(k):
        distJI=distMeas(centroids[j,:],dataSet[i,:])
        if distJI
          minDist=distJI;minIndex=j
      if clusterAssment[i,0]!=minIndex:clusterChanged=True
      clusterAssment[i,:]=minIndex,minDist**2
    print centroids
    for cent in range(k):
      ptsInClust=dataSet[np.nonzero(clusterAssment[:,0].A==cent)[0]] #[0]这里取0是指去除坐标索引值，结果会有两个
      #np.nonzero函数，寻找非0元素的下标 nz=np.nonzero([1,2,3,0,0,4,0])结果为0,1,2
      centroids[cent,:]=np.mean(ptsInClust,axis=0) 

  return centroids,clusterAssment
myCentroids,clustAssing=kMeans(dataMat,4)
print(myCentroids,clustAssing)  

#二分均值聚类(bisecting k-means)
def  biKmeans(dataSet,k,distMeas=distEclud):
  m=np.shape(dataSet)[0]
  clusterAssment=np.mat(np.zeros((m,2)))
  centroid0=np.mean(dataSet,axis=0).tolist()[0]
  centList=[centroid0]
  for j in range(m):
    clusterAssment[j,1]=distMeas(np.mat(centroid0),dataSet[j,:])**2
  while (len(centList)
    lowestSSE=np.Inf
    for i in range(len(centList)):
      ptsInCurrCluster=dataSet[np.nonzero(clusterAssment[:,0].A==i)[0],:]
      centroidMat,splitClusAss=kMeans(ptsInCurrCluster,2,distMeas)
      sseSplit=np.sum(splitClusAss[:,1])
      sseNotSplit=np.sum(clusterAssment[np.nonzero(clusterAssment[:,0].A!=i)[0],1])
      print "sseSplit, and notSplit:",sseSplit,sseNotSplit
      if (sseSplit+sseNotSplit)
        bestCenToSplit=i
        bestNewCents=centroidMat
        bestClustAss=splitClusAss.copy()
        lowestSSE=sseSplit+sseNotSplit
    bestClustAss[np.nonzero(bestClustAss[:,0].A==1)[0],0]=len(centList)
    bestClustAss[np.nonzero(bestClustAss[:,0].A==0)[0],0]=bestCenToSplit
    print "the bestCentToSplit is:",bestCenToSplit
    print 'the len of bestClustAss is:',len(bestClustAss)
    centList[bestCenToSplit]=bestNewCents[0,:]
    centList.append(bestNewCents[1,:])
    clusterAssment[np.nonzero(clusterAssment[:,0].A==bestCenToSplit)[0],:]=bestClustAss
  return centList,clusterAssment
print(u"二分聚类分析结果开始")
dataMat3=np.mat(loadDataSet(path+'testSet2.txt'))
centList,myNewAssments=biKmeans(dataMat3, 3)
print(centList)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。

您可能感兴趣的文章:

• python kmeans聚类简单介绍和实现代码
• Python实现k-means算法
• Python机器学习算法之k均值聚类（k-means）
• python实现k-means聚类算法
• Python机器学习之K-Means聚类实现详解
• Python实现Kmeans聚类算法
• python实现kMeans算法
• 详解K-means算法在Python中的实现
• python中学习K-Means和图片压缩
• Python KMeans聚类问题分析