def main():
dataSet = kMeans.loadfromcsv('./data/8.csv')
dataMat = np.mat(dataSet)
# normalize dataMat
norMat = kMeans.normalize(dataMat)
# centroids is the center of clusters
# clusterAssment[cluster_index,deviation],in which deviation represents the dist
# from current point to centroids.
# 使用Bisceting Kmeans算法对游客进行聚类，预期聚类簇数目为4
centroids, clusterAssment = kMeans.biKmeans(norMat,4)
cluster_label = clusterAssment[:,0]
clusters = [[],[],[],[]]
for i in range(0,len(cluster_label)):
clusters[(int)(cluster_label[i])].append(np.asarray(norMat)[i])
clusters = np.asarray(clusters)
for i in range(0,len(clusters)):
clusters[i] = np.asarray(clusters[i])
clusters = np.asarray(clusters)
#找出含有元素最多的簇，以及最少的簇
minCount = 10000
maxCount = 0
max_cluster = 0
min_cluster = 0
for i in range(0,len(clusters)):
if minCount > len(clusters[i]):
minCount = len(clusters[i])
if maxCount < len(clusters[i]):
maxCount = len(clusters[i])
if len(clusters[max_cluster]) < len(clusters[i]):
max_cluster = i
if len(clusters[min_cluster]) > len(clusters[i]):
min_cluster = i
print "%d cluster has %d elements " % (i, len(clusters[i])),
print "the centroids is",
print centroids[i]
number_weight = float(len(clusters[max_cluster]))/(len(clusters[min_cluster]))
print centroids[max_cluster]
# 计算Dunn指标
di = base.dunn(clusters)
# 计算N-Dunn指标
NDunnIndex = di*(maxCount/minCount)
print di
print NDunnIndex
print "original dunn is %f" % di
print "weighted dunn is %f" % (number_weight*di)
def plotCluster():
dataSetShangHai = kMeans.loadfromcsv('./data/8.csv')
dataSetXiAn = kMeans.loadfromcsv('./data/10195.csv')
dataSetQingDao = kMeans.loadfromcsv('./data/10444.csv')
dataSetSanYa = kMeans.loadfromcsv('./data/10030.csv')
dataSetJiuZhaigou = kMeans.loadfromcsv('./data/10136.csv')
dataSetTaiShan = kMeans.loadfromcsv('./data/10284.csv')
dataMatXiAn = np.mat(dataSetXiAn)
dataMatQingDao = np.mat(dataSetQingDao)
dataMatShangHai = np.mat(dataSetShangHai)
dataMatSanYa = np.mat(dataSetSanYa)
dataMatTaiShan = np.mat(dataSetTaiShan)
dataMatJiuZhaigou = np.mat(dataSetJiuZhaigou)
norMatXiAn = kMeans.normalize(dataMatXiAn)
norMatQingDao = kMeans.normalize(dataMatQingDao)
norMatShangHai = kMeans.normalize(dataMatShangHai)
norMatSanYa = kMeans.normalize(dataMatSanYa)
norMatTaiShan = kMeans.normalize(dataMatTaiShan)
norMatJiuZhaigou = kMeans.normalize(dataMatJiuZhaigou)
centroidsShangHai, clusterAssmentShangHai = kMeans.biKmeans(norMatShangHai,4)
centroidsXiAn, clusterAssmentXiAn = kMeans.biKmeans(norMatXiAn,4)
centroidsQingDao, clusterAssmentQingDao = kMeans.biKmeans(norMatQingDao,4)
centroidsSanYa, clusterAssmentSanYa = kMeans.biKmeans(norMatSanYa,4)
centroidsJiuZhaigou, clusterAssmentJiuZhaigou = kMeans.biKmeans(norMatJiuZhaigou,4)
centroidsTaiShan, clusterAssmentTaiShan = kMeans.biKmeans(norMatTaiShan,4)
print "==================上海聚类结果=========="
printBasicInfo(centroidsShangHai,clusterAssmentShangHai,norMatShangHai)
print "==================西安聚类结果=========="
printBasicInfo(centroidsXiAn, clusterAssmentXiAn, norMatXiAn)
print "==================青岛聚类结果=========="
printBasicInfo(centroidsQingDao, clusterAssmentQingDao, norMatQingDao)
print "==================三亚聚类结果=========="
printBasicInfo(centroidsSanYa, clusterAssmentSanYa, norMatSanYa)
print "==================九寨沟聚类结果========"
printBasicInfo(centroidsJiuZhaigou, clusterAssmentJiuZhaigou, norMatJiuZhaigou)
print "==================泰山聚类结果=========="
printBasicInfo(centroidsTaiShan, clusterAssmentTaiShan, norMatTaiShan)
# shanghai
plt.subplot(321)
pointClusNumShangHai = clusterAssmentShangHai[:,0].A.T
n = np.shape(pointClusNumShangHai)[1]
plt.title(u'上海')
for i in range(n):
if 0.0 == pointClusNumShangHai.item(i):
plt.plot(norMatShangHai[i,0],norMatShangHai[i,1],'g^')
elif 1.0 == pointClusNumShangHai.item(i):
plt.plot(norMatShangHai[i,0],norMatShangHai[i,1],'b*')
elif 2.0 == pointClusNumShangHai.item(i):
plt.plot(norMatShangHai[i,0],norMatShangHai[i,1],'k<')
elif 3.0 == pointClusNumShangHai.item(i):
plt.plot(norMatShangHai[i,0],norMatShangHai[i,1],'ms')
plt.plot(centroidsShangHai[:,0],centroidsShangHai[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
# plt.xlabel(u'Distance Index')
# plt.ylabel(u'Activity Degree Index')
# XiAn
plt.subplot(322)
pointClusNumXiAn = clusterAssmentXiAn[:,0].A.T
n = np.shape(pointClusNumXiAn)[1]
plt.title(u'西安')
for i in range(n):
if 0.0 == pointClusNumXiAn.item(i):
plt.plot(norMatXiAn[i,0],norMatXiAn[i,1],'g^')
elif 1.0 == pointClusNumXiAn.item(i):
plt.plot(norMatXiAn[i,0],norMatXiAn[i,1],'b*')
elif 2.0 == pointClusNumXiAn.item(i):
plt.plot(norMatXiAn[i,0],norMatXiAn[i,1],'k<')
elif 3.0 == pointClusNumXiAn.item(i):
plt.plot(norMatXiAn[i,0],norMatXiAn[i,1],'ms')
plt.plot(centroidsXiAn[:,0],centroidsXiAn[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
# plt.xlabel(u'Distance Index')
# plt.ylabel(u'Activity Degree Index')
# QingDao
plt.subplot(323)
pointClusNumQingDao = clusterAssmentQingDao[:,0].A.T
n = np.shape(pointClusNumQingDao)[1]
plt.title(u'青岛')
for i in range(n):
if 0.0 == pointClusNumQingDao.item(i):
plt.plot(norMatQingDao[i,0],norMatQingDao[i,1],'g^')
elif 1.0 == pointClusNumQingDao.item(i):
plt.plot(norMatQingDao[i,0],norMatQingDao[i,1],'b*')
elif 2.0 == pointClusNumQingDao.item(i):
plt.plot(norMatQingDao[i,0],norMatQingDao[i,1],'k<')
elif 3.0 == pointClusNumQingDao.item(i):
plt.plot(norMatQingDao[i,0],norMatQingDao[i,1],'ms')
plt.plot(centroidsQingDao[:,0],centroidsQingDao[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
# plt.xlabel(u'Distance Index')
# plt.ylabel(u'Activity Degree Index')
# SanYa
plt.subplot(324)
pointClusNumSanYa = clusterAssmentSanYa[:,0].A.T
n = np.shape(pointClusNumSanYa)[1]
plt.title(u'三亚')
for i in range(n):
if 0.0 == pointClusNumSanYa.item(i):
plt.plot(norMatSanYa[i,0],norMatSanYa[i,1],'g^')
elif 1.0 == pointClusNumSanYa.item(i):
plt.plot(norMatSanYa[i,0],norMatSanYa[i,1],'b*')
elif 2.0 == pointClusNumSanYa.item(i):
plt.plot(norMatSanYa[i,0],norMatSanYa[i,1],'k<')
elif 3.0 == pointClusNumSanYa.item(i):
plt.plot(norMatSanYa[i,0],norMatSanYa[i,1],'ms')
plt.plot(centroidsSanYa[:,0],centroidsSanYa[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
# plt.xlabel(u'Distance Index')
# plt.ylabel(u'Activity Index')
# JiuZhaigou
plt.subplot(325)
pointClusNumJiuZhaigou = clusterAssmentJiuZhaigou[:,0].A.T
n = np.shape(pointClusNumJiuZhaigou)[1]
plt.title(u'九寨沟')
for i in range(n):
if 0.0 == pointClusNumJiuZhaigou.item(i):
plt.plot(norMatJiuZhaigou[i,0],norMatJiuZhaigou[i,1],'g^')
elif 1.0 == pointClusNumJiuZhaigou.item(i):
plt.plot(norMatJiuZhaigou[i,0],norMatJiuZhaigou[i,1],'b*')
elif 2.0 == pointClusNumJiuZhaigou.item(i):
plt.plot(norMatJiuZhaigou[i,0],norMatJiuZhaigou[i,1],'k<')
elif 3.0 == pointClusNumJiuZhaigou.item(i):
plt.plot(norMatJiuZhaigou[i,0],norMatJiuZhaigou[i,1],'ms')
plt.plot(centroidsJiuZhaigou[:,0],centroidsJiuZhaigou[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
plt.xlabel(u'Distance Index')
plt.ylabel(u'Activity Index')
# TaiShan
plt.subplot(326)
pointClusNumTaiShan = clusterAssmentTaiShan[:,0].A.T
n = np.shape(pointClusNumTaiShan)[1]
plt.title(u'泰山')
for i in range(n):
if 0.0 == pointClusNumTaiShan.item(i):
plt.plot(norMatTaiShan[i,0],norMatTaiShan[i,1],'g^')
elif 1.0 == pointClusNumTaiShan.item(i):
plt.plot(norMatTaiShan[i,0],norMatTaiShan[i,1],'b*')
elif 2.0 == pointClusNumTaiShan.item(i):
plt.plot(norMatTaiShan[i,0],norMatTaiShan[i,1],'k<')
elif 3.0 == pointClusNumTaiShan.item(i):
plt.plot(norMatTaiShan[i,0],norMatTaiShan[i,1],'ms')
plt.plot(centroidsTaiShan[:,0],centroidsTaiShan[:,1],'ro')
plt.axis([0,4.0,0,12])
plt.xticks([0,1,2,3])
plt.xlabel(u'Distance Index')
plt.ylabel(u'Activity Index')
plt.show()
