python实现kMeans算法
聚类是一种无监督的学习,将相似的对象放到同一簇中,有点像是全自动分类,簇内的对象越相似,簇间的对象差别越大,则聚类效果越好。 1、k均值聚类算法 k均值聚类将数据分为k个簇,每个簇通过其质心,即簇中所有点的中心来描述。首先随机确定k个初始点作为质心,然后将数据集分配到距离最近的簇中。然后将每个簇的质心更新为所有数据集的平均值。然后再进行第二次划分数据集,直到聚类结果不再变化为止。 伪代码为 随机创建k个簇质心 python实现 import numpy as np import matplotlib.pyplot as plt def loadDataSet(fileName): dataMat = [] with open(fileName) as f: for line in f.readlines(): line = line.strip().split('t') dataMat.append(line) dataMat = np.array(dataMat).astype(np.float32) return dataMat def distEclud(vecA,vecB): return np.sqrt(np.sum(np.power((vecA-vecB),2))) def randCent(dataSet,k): m = np.shape(dataSet)[1] center = np.mat(np.ones((k,m))) for i in range(m): centmin = min(dataSet[:,i]) centmax = max(dataSet[:,i]) center[:,i] = centmin + (centmax - centmin) * np.random.rand(k,1) return center def kMeans(dataSet,k,distMeans = 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 for j in range(k): distJI = distMeans(dataSet[i,:],centroids[j,:]) if distJI < minDist: minDist = distJI minIndex = j if clusterAssment[i,0] != minIndex: clusterChanged = True clusterAssment[i,:] = minIndex,minDist**2 for cent in range(k): ptsInClust = dataSet[np.nonzero(clusterAssment[:,0].A == cent)[0]] centroids[cent,:] = np.mean(ptsInClust,axis = 0) return centroids,clusterAssment data = loadDataSet('testSet.txt') muCentroids,clusterAssing = kMeans(data,4) fig = plt.figure(0) ax = fig.add_subplot(111) ax.scatter(data[:,0],data[:,1],c = clusterAssing[:,0].A) plt.show() print(clusterAssing) 2、二分k均值算法 K均值算法可能会收敛到局部最小值,而非全局最小。一种用于度量聚类效果的指标为误差平方和(SSE)。因为取了平方,更加重视原理中心的点。为了克服k均值算法可能会收敛到局部最小值的问题,有人提出来二分k均值算法。 伪代码 将所有点看成一个簇 python实现 import numpy as np import matplotlib.pyplot as plt def loadDataSet(fileName): dataMat = [] with open(fileName) as f: for line in f.readlines(): line = line.strip().split('t') dataMat.append(line) dataMat = np.array(dataMat).astype(np.float32) return dataMat def distEclud(vecA,clusterAssment def biKmeans(dataSet,distMeans = distEclud): m = np.shape(dataSet)[0] clusterAssment = np.mat(np.zeros((m,2))) centroid0 = np.mean(dataSet,axis=0).tolist() centList = [centroid0] for j in range(m): clusterAssment[j,1] = distMeans(dataSet[j,np.mat(centroid0))**2 while (len(centList)<k): lowestSSE = np.inf for i in range(len(centList)): ptsInCurrCluster = dataSet[np.nonzero(clusterAssment[:,0].A == i)[0],:] centroidMat,splitClustAss = kMeans(ptsInCurrCluster,2,distMeans) sseSplit = np.sum(splitClustAss[:,1]) sseNotSplit = np.sum(clusterAssment[np.nonzero(clusterAssment[:,0].A != i)[0],1]) if (sseSplit + sseNotSplit) < lowestSSE: bestCentToSplit = i bestNewCents = centroidMat.copy() bestClustAss = splitClustAss.copy() lowestSSE = sseSplit + sseNotSplit print('the best cent to split is ',bestCentToSplit) # print('the len of the bestClust') bestClustAss[np.nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) bestClustAss[np.nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit clusterAssment[np.nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:] = bestClustAss.copy() centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0] centList.append(bestNewCents[1,:].tolist()[0]) return np.mat(centList),clusterAssment data = loadDataSet('testSet2.txt') muCentroids,clusterAssing = biKmeans(data,3) fig = plt.figure(0) ax = fig.add_subplot(111) ax.scatter(data[:,0].A,cmap=plt.cm.Paired) ax.scatter(muCentroids[:,muCentroids[:,1]) plt.show() print(clusterAssing) print(muCentroids) 代码及数据集下载:K-means 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编程小技巧。 (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |