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)

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