python机器学习聚类实例之31个省份的居民消费聚类（包含数据）

居民消费聚类（包含数据）"/>

python机器学习聚类实例之31个省份的居民消费聚类（包含数据）

数据介绍

现有1999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个主要变量数据,这八个变量分别是:食品、衣着、家庭设备用品及服务、医疗保健、交通和通讯、娱乐教育文化服务、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。

实际数据（直接复制到当前工程下的city.txt文件中）

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,1375.46,480.89,273.84,317.32,251.08,424.75,228.73,195.93
新疆,1608.82,536.05,432.46,235.82,250.28,541.30,344.85,214.40

实验目的

通过聚类，了解1999年各个省份的消费水平在国内的情况

实现代码和注解

• 输出的给各个城市打上的标签

[3 1 2 2 2 2 2 2 3 0 1 0 1 2 2 2 0 0 3 0 0 1 0 2 0 1 2 2 2 2 2]

• 形成的簇的中心的数组：关于这里就不是很懂，32个数据，四组是因为有四个簇，而且将下述的数据当做expense，这是什么理由？

 [[2004.785       429.48        347.8925      190.955       287.66625581.16125     437.2375      233.09625   ][2549.658       582.118       488.366       268.998       397.442618.92        477.946       295.172     ][1525.81533333  478.672       322.88266667  232.4         236.41866667457.53133333  344.81866667  190.21933333][3242.22333333  544.92        735.78        405.51333333  602.251016.62        760.52333333  446.82666667]]

• 具体的代码以及注解

import numpy as np
from sklearn.cluster import KMeansdef loadData(filePath):# 打开文件流fr = open(filePath, 'r+',encoding='UTF-8')# 完整的读取，一次性读取整个文件，按行读取lines = fr.readlines()# 将读入的数据进行拆分，分为数据和城市名retData = []retCityName = []for line in lines:# 去除字符串首尾的空格或者回车，并使用“，”进行分割items = line.strip().split(",")# 每一行的开头是城市名臣retCityName.append(items[0])# 将数据组合成一个列表，并且强制转换类型为float浮点型retData.append([float(items[i]) for i in range(1, len(items))])return retData, retCityNameif __name__ == '__main__':# 使用读取数据，获取城市名和相关的数据data, cityName = loadData('city.txt')# 创建指定簇数量KMeans对象实例km = KMeans(n_clusters=4)# 加载数据，进行训练，获得标签，总共是四个簇，就是四个标签，将给31个数据，每个数据都打上0-3的标签label = km.fit_predict(data)# 计算出每一个簇形成的所有的行内的数据，计算出该簇内的数据的和expenses = np.sum(km.cluster_centers_, axis=1)# 总共是四个标签，四个集合，按照打上的标签将城市名进行分类CityCluster = [[], [], [], []]# 遍历所有的标签，并将对应的城市根据标签加上对应的簇中for i in range(len(cityName)):CityCluster[label[i]].append(cityName[i])# 遍历所有的簇中心的数量，总共就只有四个，进行打印输出for i in range(len(CityCluster)):print("Expenses:%.2f" % expenses[i])print(CityCluster[i])

• 运行结果