优点:容易实现
缺点:容易陷入局部最小值,在大规模数据收敛很慢。
适用数据类型:数值型数据
from numpy import *
def loadDataSet(fileName): #加载数据
dataMat = [] #assume last column is target value
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
fltLine = map(float,curLine) #map all elements to float()
dataMat.append(fltLine)
return dataMat
def distEclud(vecA, vecB):
return sqrt(sum(power(vecA - vecB, 2))) #计算两个向量的欧氏距离
def randCent(dataSet, k): #随机产生k个在数据范围内的中心点,并返回
n = shape(dataSet)[1]
centroids = mat(zeros((k,n)))#create centroid mat
for j in range(n):#create random cluster centers, within bounds of each dimension
minJ = min(dataSet[:,j])
rangeJ = float(max(dataSet[:,j]) - minJ)
centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))
return centroids
训练函数
def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
m = shape(dataSet)[0] #获取样本数
clusterAssment = mat(zeros((m,2)))#记录样本被分配到哪个中心点,第二列记录到最近中心点的欧氏距离
centroids = createCent(dataSet, k) #随机产生k个中心点
clusterChanged = True
while clusterChanged:
clusterChanged = False
for i in range(m): #对每个样本寻找与其最近的中心点
minDist = inf; minIndex = -1
for j in range(k): #对每个样本寻找与其最近的中心点匹配
distJI = distMeas(centroids[j,:],dataSet[i,:])
if distJI < minDist:
minDist = distJI; minIndex = j
if clusterAssment[i,0] != minIndex: clusterChanged = True #判断分配是否发生改变
clusterAssment[i,:] = minIndex,minDist**2
print(centroids)
for cent in range(k):#recalculate centroids
ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#获取分到同一类的样本
centroids[cent,:] = mean(ptsInClust, axis=0) #求分配到同一类样本的均值
return centroids, clusterAssment
二分Kmeans聚类
原理
代码
def biKmeans(dataSet, k, distMeas=distEclud):
m = shape(dataSet)[0]
clusterAssment = mat(zeros((m,2)))
centroid0 = mean(dataSet, axis=0).tolist()[0]
centList =[centroid0] #create a list with one centroid
for j in range(m):#calc initial Error
clusterAssment[j,1] = distMeas(mat(centroid0), dataSet[j,:])**2
while (len(centList) < k):
lowestSSE = inf
for i in range(len(centList)):
ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#获取分为i类的数据
centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas) #对同一类再进行内部二分类
sseSplit = sum(splitClustAss[:,1])#获取二分类之后的SSE误差平方差
sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:,0].A!=i)[0],1])#获取二分类之前的SSE误差平方差
print("sseSplit, and notSplit: ",sseSplit,sseNotSplit)
if (sseSplit + sseNotSplit) < lowestSSE:
bestCentToSplit = i
bestNewCents = centroidMat
bestClustAss = splitClustAss.copy()
lowestSSE = sseSplit + sseNotSplit
bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever
bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit
print ('the bestCentToSplit is: ',bestCentToSplit)
print ('the len of bestClustAss is: ', len(bestClustAss))
centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids
centList.append(bestNewCents[1,:].tolist()[0])
clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE
return mat(centList), clusterAssment