机器学习笔记－－聚类算法 k-means－－31省市消费水平聚类

参考文章：https://blog.csdn.net/rankiy/article/details/99843363

1.数据集

数据介绍：
现有1999年全国31个省份城镇居民家庭平均每月全年消费性支出 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、服务、医疗保健、交通、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。

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 

2.k-means算法分析

k-mearns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，而簇间的相似度较低
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，将其归入最近的簇
3、对每个簇，计算所有点的均值作为新的取类中心
4、重复2、3直到取类中心不再发生改变

3.实验过程

1、使用算法：K-means聚类算法
2、实现过程：
1、建立工程，导入sklearn相关包
import numpy as np
from sklearn.cluster import KMeans
2、加载数据，创建K-means算法实例，并进行训练，获得标签
3、输出标签，查看结果
将城市按照消费水平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

调用K-Means方法所需参数：
km=KMeans(n_clusters=8,init=‘k-means++’,n_init=10,max_iter=300,tol=1e-4,
precompute_distances=‘auto’,verbose=0,random_state=None,
copy_x=True,n_jobs=None,algorithm=‘auto’)
n_clusters:用于指定取类中心的个数
init:初始聚类中心的初始化方法
max_iter:最大迭代次数
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

其他参数：
data:加载的数据
label:聚类后各数据所属的标签
fit_predict():计算簇中心以及为簇分配序号
 

4.实现代码

import numpy as np
from sklearn.cluster import KMeans

def loadData(filePath):
    fr = open(filePath,'r+')
    lines = fr.readlines()
    retData = []
    retCityName = []
    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

if __name__=='__main__':
    filepath = r'C:\Users\l\Desktop\city.txt'
    data,cityName=loadData(filepath)  #利用loadData方法读取数据
    km=KMeans(n_clusters=4)              #创建实例
    label=km.fit_predict(data)          #调用Kmeans() fit_predict()方法进行计算
    print(km.cluster_centers_)
    expenses=np.sum(km.cluster_centers_,axis=1)  
    #print(expenses)
    CityCluster=[[],[],[],[]]      #将城市 按label分成设定的簇，将每个簇的城市输出
    for i in range(len(cityName)):
        CityCluster[label[i]].append(cityName[i])
    for i in range(len(CityCluster)):
        print("Expenses:{}.2f" .format(expenses[i]))
        print(CityCluster[i])