K-means聚类算法的应用——Python数据工程No.5

k-means算法：以k为参数，把n个对象分成k个簇，使簇内具有较高的相似度，簇间的相似度较低。

步骤：
1.随机选择k个点作为初始的聚类中心；
2.对于剩下的点，根据其与聚类中心的距离，将其归于最近的簇；
3.对每个簇计算所有点的均值作为新的聚类中心；
4.重复步骤2、3，知道聚类中心不再改变。

实现方法：
sklearn.cluster.Kmeans

案例：
根据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数据按照消费水平进行聚类分析
数据：

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

代码：

# -*- coding: utf-8 -*-
"""
Created on Sun Dec 30 13:46:21 2018

@author: MXX
"""

#导入numpy包
import numpy as np
#从sklearn库的聚类模块中导入KMeans包
from sklearn.cluster import KMeans
 
#定义加载数据函数
#函数名为loadData，函数参数为文件路径
def loadData(filePath):
    #以r+的方式打开 +表示打开磁盘文件更新(阅读和写作)
    fr = open(filePath,'r+')
    #以readlines方式读取文件
    '''
    readlines() 方法用于读取所有行(直到结束符 EOF)并返回列表，
    该列表可以由 Python 的 for... in ... 结构进行处理。
    如果碰到结束符 EOF 则返回空字符串。
    '''
    lines = fr.readlines()
    #数据列表
    retData = []
    #城市名称列表
    retCityName = []
    #循环遍历
    for line in lines:
        items = line.strip().split(",")
        retCityName.append(items[0])
        retData.append([float(items[i]) for i in range(1,len(items))])
        #返回值是城市名称和具体数据
    return retData,retCityName
 
     
if __name__ == '__main__':
    data,cityName = loadData('city.txt')
    #定义4个簇
    km = KMeans(n_clusters=4)
    #计算簇中心以及为簇分配序号
    label = km.fit_predict(data)
    #计算花费
    expenses = np.sum(km.cluster_centers_,axis=1)
    #print(expenses)
    #定义二维列表，按类盛放城市名称
    CityCluster = [[],[],[],[]]
    #将城市按照label分成设定的簇
    for i in range(len(cityName)):
        CityCluster[label[i]].append(cityName[i])
    #将城市名输出，将城市的花费输出
    for i in range(len(CityCluster)):
        print("Expenses:%.2f" % expenses[i])
        print(CityCluster[i])

运行结果：