K-means是一种聚类算法：
这里运用k-means进行31个城市的分类
城市的数据保存在city.txt文件中，内容如下：
BJ,2959.19,730.79,749.41,513.34,467.87,1141.82,478.42,457.64
TianJin,2459.77,495.47,697.33,302.87,284.19,735.97,570.84,305.08
HeBei,1495.63,515.90,362.37,285.32,272.95,540.58,364.91,188.63
ShanXi,1406.33,477.77,290.15,208.57,201.50,414.72,281.84,212.10
NMG,1303.97,524.29,254.83,192.17,249.81,463.09,287.87,192.96
LiaoNing,1730.84,553.90,246.91,279.81,239.18,445.20,330.24,163.86
JiLin,1561.86,492.42,200.49,218.36,220.69,459.62,360.48,147.76
HLJ,1410.11,510.71,211.88,277.11,224.65,376.82,317.61,152.85
ShangHai,3712.31,550.74,893.37,346.93,527.00,1034.98,720.33,462.03
JiangSu,2207.58,449.37,572.40,211.92,302.09,585.23,429.77,252.54
ZheJiang,2629.16,557.32,689.73,435.69,514.66,795.87,575.76,323.36
AnHui,1844.78,430.29,271.28,126.33,250.56,513.18,314.00,151.39
FuJian,2709.46,428.11,334.12,160.77,405.14,461.67,535.13,232.29
JiangXi,1563.78,303.65,233.81,107.90,209.70,393.99,509.39,160.12
ShanDong,1675.75,613.32,550.71,219.79,272.59,599.43,371.62,211.84
HeNan,1427.65,431.79,288.55,208.14,217.00,337.76,421.31,165.32
HuNan,1942.23,512.27,401.39,206.06,321.29,697.22,492.60,226.45
HuBei,1783.43,511.88,282.84,201.01,237.60,617.74,523.52,182.52
GuangDong,3055.17,353.23,564.56,356.27,811.88,873.06,1082.82,420.81
GuangXi,2033.87,300.82,338.65,157.78,329.06,621.74,587.02,218.27
HaiNan,2057.86,186.44,202.72,171.79,329.65,477.17,312.93,279.19
ChongQing,2303.29,589.99,516.21,236.55,403.92,730.05,438.41,225.80
SiChuang,1974.28,507.76,344.79,203.21,240.24,575.10,430.36,223.46
GuiZhou,1673.82,437.75,461.61,153.32,254.66,445.59,346.11,191.48
YunNan,2194.25,537.01,369.07,249.54,290.84,561.91,407.70,330.95
XiZang,2646.61,839.70,204.44,209.11,379.30,371.04,269.59,389.33
SHanXi,1472.95,390.89,447.95,259.51,230.61,490.90,469.10,191.34
GanSu,1525.57,472.98,328.90,219.86,206.65,449.69,249.66,228.19
QingHai,1654.69,437.77,258.78,303.00,244.93,479.53,288.56,236.51
NingXia,1375.46,480.89,273.84,317.32,251.08,424.75,228.73,195.93
XinJiang,1608.82,536.05,432.46,235.82,250.28,541.30,344.85,214.40
本来数据的头一个是中文的，但是由于中文读取需要解码，出了一些问题，索性改成了城市名字的拼音，每一行都是一个城市的数据
然后把city.txt 文件保存到路径文件夹下。这个文件夹是根据编辑软件设定的，我用的是spyder，然后建立了一个工程，就把city.txt文
件考到了工程目录下。
之后在工程中输入一下程序：
'''
created on Wed Jul 05 09:13:43 2017
author: GXTon
email ：g159147t@163.com
jiaotashidi qiuzhenwushi
'''
#
import numpy as np #要用k-means算法，需要导入numpy
from sklearn.cluster import KMeans #只导入一部分，
def loadData(filePath): #创建一个读取数据的函数
fr = open(filePath,'r+') #这里是去读
lines = fr.readlines() #.read()每次读取整个文件，通常用于将文件内容放到一个字符串变量中
#.readlines()一次读取整个文件（类似于.resd()）
#.readline()每次只读取一行，通常比.readlines()慢得多。仅当没有足够内存时才使用它。
retData = [] #用于存储城市的各项消费信息
retCityName = [] #用于存储城市名称
for line in lines:
items = line.strip().split(",")
retCityName.append(items[0])
retData.append([float(items[i]) for i in range(1,len(items))])
return retData,retCityName #返回值：返回城市名称，以及该城市的各项消费信息。
if __name__ == '__main__': #这里相当于主函数
data,cityName = loadData('city.txt') #利用loadData方法读取数据，加载数据
km = KMeans(n_clusters=4) #创建实例，创建k-means算法，这里把所有分成4组；
#调用k-means方法所需参数：n_clusters,用于指定聚类中心的个数
#init，初始聚类中心的初始化方法
#max_iter，最大的迭代次数
#一般调用时只用给出n_clusters即可，init默认是k-means++,max_iter默认是300
label = km.fit_predict(data) #调用Kmeans（）fit_predict（）方法进行计算，
#作用是计算簇中心以及为为簇分配符号，label：聚类后个数据所属的标签。
expenses = np.sum(km.cluster_centers_,axis=1) #axis按行求和
#print(expenses)
CityCluster = [[],[],[],[]]
for i in range(len(cityName)): #将城市按照label分成设定的簇
CityCluster[label[i]].append(cityName[i]) #将每个簇的城市输出
for i in range(len(CityCluster)):
print("Expenses:%.2f" % expenses[i]) #将每个簇的平均花费输出
print(CityCluster[i])
点击运行，便能出来结果。
其中n_clusters类，消费水平相近的城市聚集在一类中
expense：聚类中心点的数值加和，也就是平均消费水平
实现过程：
1、建立工程，导入sklearn相关包
import numpy as np
from sklearn.cluster import KMeans
