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python實(shí)現(xiàn)kMeans算法

更新時(shí)間：2017年12月21日 10:58:43 作者：開貳錘

這篇文章主要為大家詳細(xì)介紹了python實(shí)現(xiàn)kMeans算法，具有一定的參考價(jià)值，感興趣的小伙伴們可以參考一下

聚類是一種無監(jiān)督的學(xué)習(xí)，將相似的對(duì)象放到同一簇中，有點(diǎn)像是全自動(dòng)分類，簇內(nèi)的對(duì)象越相似，簇間的對(duì)象差別越大，則聚類效果越好。

1、k均值聚類算法

k均值聚類將數(shù)據(jù)分為k個(gè)簇，每個(gè)簇通過其質(zhì)心，即簇中所有點(diǎn)的中心來描述。首先隨機(jī)確定k個(gè)初始點(diǎn)作為質(zhì)心，然后將數(shù)據(jù)集分配到距離最近的簇中。然后將每個(gè)簇的質(zhì)心更新為所有數(shù)據(jù)集的平均值。然后再進(jìn)行第二次劃分?jǐn)?shù)據(jù)集，直到聚類結(jié)果不再變化為止。

偽代碼為

隨機(jī)創(chuàng)建k個(gè)簇質(zhì)心
當(dāng)任意一個(gè)點(diǎn)的簇分配發(fā)生改變時(shí)：
    對(duì)數(shù)據(jù)集中的每個(gè)數(shù)據(jù)點(diǎn)：
        對(duì)每個(gè)質(zhì)心：
            計(jì)算數(shù)據(jù)集到質(zhì)心的距離
        將數(shù)據(jù)集分配到最近距離質(zhì)心對(duì)應(yīng)的簇
    對(duì)每一個(gè)簇，計(jì)算簇中所有點(diǎn)的均值并將均值作為質(zhì)心

python實(shí)現(xiàn)

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均值算法可能會(huì)收斂到局部最小值，而非全局最小。一種用于度量聚類效果的指標(biāo)為誤差平方和（SSE）。因?yàn)槿×似椒?，更加重視原理中心的點(diǎn)。為了克服k均值算法可能會(huì)收斂到局部最小值的問題，有人提出來二分k均值算法。
首先將所有點(diǎn)作為一個(gè)簇，然后將該簇一分為二，然后選擇所有簇中對(duì)其劃分能夠最大程度減低SSE的值的簇，直到滿足指定簇?cái)?shù)為止。

偽代碼

將所有點(diǎn)看成一個(gè)簇
計(jì)算SSE
while 當(dāng)簇?cái)?shù)目小于k時(shí)：
    for 每一個(gè)簇：
        計(jì)算總誤差
        在給定的簇上進(jìn)行k均值聚類(k=2)
        計(jì)算將該簇一分為二的總誤差
    選擇使得誤差最小的那個(gè)簇進(jìn)行劃分操作

python實(shí)現(xiàn)

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

def biKmeans(dataSet,k,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],data[:,1],c = clusterAssing[:,0].A,cmap=plt.cm.Paired)
ax.scatter(muCentroids[:,0],muCentroids[:,1])
plt.show()

print(clusterAssing)
print(muCentroids)

代碼及數(shù)據(jù)集下載：K-means

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