使用 k-means 聚類算法對多維屬性數據進行分類

數據形式如下：

 

 前期數據整合：

import pandas as pd
import scipy 
import scipy.cluster.hierarchy as sch 
from scipy.cluster.vq import vq,kmeans,whiten 
import numpy as np 
import matplotlib.pylab as plt

df1 = pd.read_csv(r"D:\01RiverPro\01DATA\01Headwater\CSV\dem.csv")
df2 = pd.read_csv(r"D:\01RiverPro\01DATA\01Headwater\CSV\ndvi_mean.csv")
df3 = pd.read_csv(r"D:\01RiverPro\01DATA\01Headwater\CSV\pop_mean.csv")
result = pd.merge(df1, df2, how='inner', on=['GRIDCODE'])#取交集
result = pd.merge(result, df3, how='inner', on=['GRIDCODE'])
df=result[['GRIDCODE','dem_mean','ndvi_mean','pop_mean']]
#新增一列其他方法進行的分類標簽
ishw = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
df['Headwater_label'] = ishw 
#轉為array
dataset = df.values
points = dataset [:,1:4]#第2列到第4屬性列
ishw_label = dataset[:,-1]
#print("points:\n",points)

# k-means聚類
#將原始數據做歸一化處理
data=whiten(points)
#使用kmeans函數進行聚類,輸入第一維為數據,第二維為聚類個數k.
#有些時候我們可能不知道最終究竟聚成多少類,一個辦法是用層次聚類的結果進行初始化.當然也可以直接輸入某個數值. 
#k-means最后輸出的結果其實是兩維的,第一維是聚類中心,第二維是損失distortion,我們在這里只取第一維,所以最后有個[0]
#centroid = kmeans(data,max(cluster))[0]  
centroid = kmeans(data,2)[0]#分為2類
print(centroid)#輸出中心
#使用vq函數根據聚類中心對所有數據進行分類,vq的輸出也是兩維的,[0]表示的是所有數據的label
label=vq(data,centroid)[0]
label


#輸出兩類的數量
num = [0,0]
for i in label:
    if(i == 0):
        num[0] = num[0] + 1
    else:
        num[1] = num[1] + 1
print('num =',num)


#輸出符合預期的比例等
print("Final clustering by k-means:\n",label)
result = np.subtract(label,ishw_label)
print("result:\n",result)

count = [0,0]
for i in result:
    if(i == 0):
        count[0] = count[0] + 1
    else:
        count[1] = count[1] + 1
print(count)
print(float(count[0])/(count[0]+count[1]))

　　

 

 

 

 

 

 

 

 

參考：
https://blog.csdn.net/wukai0909/article/details/72639174