Kmeans算法执行原理浅析
k-meansmeans 算法以 k为参数，把 n个对象分成 k个簇，使内具有较高的相似 度，而簇间的相似较低。
其处理过程如下：
1. 随机选择 k个点作为初始的聚类中心；
2. 对于剩下的点，根据其与聚类中心距离将归入最近簇
3. 对每个簇，计算所有点的均值作为新聚类中心
4. 重复 2、3直到聚类中心不再发生改变
具体算法详情请参考西瓜书---
注：我这里用的numpy scipy matplotlib sklearn库安装是用pip install xxx安装的，速度奇慢，平均就36kb/s。网上说的那些镜像尝试过，速度也不快。
下面是本次实验的数据集以及实现代码：
数据介绍：
现有 1999 年全国 年全国 年全国 31 个省份城镇居民家庭平均每人全年消费性支出的八主 个省份城镇居民家庭平均每人全年消费性支出的八主 个省份城镇居民家庭平均每人全年消费性支出的八主 个省份城镇居民家庭平均每人全年消费性支出的八主 要变量数据 要变量数据 ，这八个变量分别是：食品 这八个变量分别是：食品 这八个变量分别是：食品 这八个变量分别是：食品 、衣着 、家庭设备用品及服务 家庭设备用品及服务 家庭设备用品及服务 家庭设备用品及服务 家庭设备用品及服务 、医疗 保健 、交通和讯 交通和讯 交通和讯 、娱乐教育文化服务 娱乐教育文化服务 娱乐教育文化服务 娱乐教育文化服务 、居住以及杂项商品和服务 居住以及杂项商品和服务 居住以及杂项商品和服务 居住以及杂项商品和服务 。利用已 有数据 ，对31 个省份进行聚类 。
北京,2959.19,730.79,749.41,513.34,467.87,1141.82,478.42,457.64
天津,2459.77,495.47,697.33,302.87,284.19,735.97,570.84,305.08
河北,1495.63,515.90,362.37,285.32,272.95,540.58,364.91,188.63
山西,1406.33,477.77,290.15,208.57,201.50,414.72,281.84,212.10
内蒙古,1303.97,524.29,254.83,192.17,249.81,463.09,287.87,192.96
辽宁,1730.84,553.90,246.91,279.81,239.18,445.20,330.24,163.86
吉林,1561.86,492.42,200.49,218.36,220.69,459.62,360.48,147.76
黑龙江,1410.11,510.71,211.88,277.11,224.65,376.82,317.61,152.85
上海,3712.31,550.74,893.37,346.93,527.00,1034.98,720.33,462.03
江苏,2207.58,449.37,572.40,211.92,302.09,585.23,429.77,252.54
浙江,2629.16,557.32,689.73,435.69,514.66,795.87,575.76,323.36
安徽,1844.78,430.29,271.28,126.33,250.56,513.18,314.00,151.39
福建,2709.46,428.11,334.12,160.77,405.14,461.67,535.13,232.29
江西,1563.78,303.65,233.81,107.90,209.70,393.99,509.39,160.12
山东,1675.75,613.32,550.71,219.79,272.59,599.43,371.62,211.84
河南,1427.65,431.79,288.55,208.14,217.00,337.76,421.31,165.32
湖南,1942.23,512.27,401.39,206.06,321.29,697.22,492.60,226.45
湖北,1783.43,511.88,282.84,201.01,237.60,617.74,523.52,182.52
广东,3055.17,353.23,564.56,356.27,811.88,873.06,1082.82,420.81
广西,2033.87,300.82,338.65,157.78,329.06,621.74,587.02,218.27
海南,2057.86,186.44,202.72,171.79,329.65,477.17,312.93,279.19
重庆,2303.29,589.99,516.21,236.55,403.92,730.05,438.41,225.80
四川,1974.28,507.76,344.79,203.21,240.24,575.10,430.36,223.46
贵州,1673.82,437.75,461.61,153.32,254.66,445.59,346.11,191.48
云南,2194.25,537.01,369.07,249.54,290.84,561.91,407.70,330.95
西藏,2646.61,839.70,204.44,209.11,379.30,371.04,269.59,389.33
陕西,1472.95,390.89,447.95,259.51,230.61,490.90,469.10,191.34
甘肃,1525.57,472.98,328.90,219.86,206.65,449.69,249.66,228.19
青海,1654.69,437.77,258.78,303.00,244.93,479.53,288.56,236.51
宁夏,1375.46,480.89,273.84,317.32,251.08,424.75,228.73,19，5.93
新疆,1608.82,536.05,432.46,235.82,250.28,541.30,344.85,214.40
上述数据集粘贴至txt文档中，用于后续代码实现
###Kmeans--python 3.7
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
##load data
def getdata(path):
f=open(path,"r+")
lines=f.readlines()
Data=[]
Cityname=[]
##逐行进行数据处理
for line in lines:
items=line.strip().split(",")
print(len(items))
Cityname.append(items[0])
Data.append([float(items[i]) for i in range(1,len(items))])
return Data,Cityname
if __name__=='__main__':
data,cityname=getdata('C:/Users/Administrator/Desktop/city.txt')
kmeans=KMeans(n_clusters=4)
label=kmeans.fit_predict(data)
##[0 2 3 3 3 3 3 3 0 1 2 1 2 3 3 3 1 1 0 1 1 2 1 3 1 2 3 3 3 3 3]
print(label)
print(kmeans.cluster_centers_)
exp=np.sum(kmeans.cluster_centers_,axis=1)
print(exp)
citycluster=[[],[],[],[]]
for i in range(len(cityname)):
citycluster[label[i]].append(cityname[i])
for i in range(len(citycluster)):
print("expense:%.2f"%exp[i])
print(citycluster[i])
##绘制省份消费总额散点图
x_label=[i for i in range(len(cityname))]
y_label=np.sum(data,axis=1)
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
for i in range(len(cityname)):
if label[i]==0:
plt.scatter(x_label[i],y_label[i],c='r')
elif label[i]==1:
plt.scatter(x_label[i],y_label[i],c='g')
elif label[i]==2:
plt.scatter(x_label[i],y_label[i],c='b')
elif label[i]==3:
plt.scatter(x_label[i],y_label[i],c='m')
##为每个数据点添加标签
for i in range(len(x_label)):
plt.annotate(cityname[i],xy=(x_label[i],y_label[i]),xytext=(x_label[i]+0.1,y_label[i]+0.1))
plt.show()
实验结果
--------------------------------------------------------------------------------------------------------------------参考mooc网课，北理工python机器学习应用课程