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：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
|
#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服务器之家。
原文链接：http://blog.csdn.net/luoyexuge/article/details/49105177