python kmeans聚類簡(jiǎn)單介紹和實(shí)現(xiàn)代碼

更新時(shí)間：2018年02月23日 10:42:39 作者：Jason____zhou

這篇文章主要為大家詳細(xì)介紹了python kmeans聚類簡(jiǎn)單介紹和實(shí)現(xiàn)代碼，具有一定的參考價(jià)值，感興趣的小伙伴們可以參考一下

一、k均值聚類的簡(jiǎn)單介紹

假設(shè)樣本分為c類，每個(gè)類均存在一個(gè)中心點(diǎn)，通過隨機(jī)生成c個(gè)中心點(diǎn)進(jìn)行迭代，計(jì)算每個(gè)樣本點(diǎn)到類中心的距離（可以自定義、常用的是歐式距離）

將該樣本點(diǎn)歸入到最短距離所在的類，重新計(jì)算聚類中心，進(jìn)行下次的重新劃分樣本，最終類中心不改變時(shí)，聚類完成

二、偽代碼

三、python代碼實(shí)現(xiàn)

#!/usr/bin/env python 
# coding=utf-8 
 
import numpy as np 
import random 
import matplotlib.pyplot as plt 
 
#data:numpy.array dataset 
#k the number of cluster 
def k_means(data,k): 
   
  #random generate cluster_center 
  sample_num=data.shape[0] 
  center_index=random.sample(range(sample_num),k) 
  cluster_cen=data[center_index,:] 
 
  is_change=1 
  cat=np.zeros(sample_num) 
   
 
  while is_change: 
    is_change=0 
 
    for i in range(sample_num): 
      min_distance=100000 
      min_index=0 
 
      for j in range(k): 
        sub_data=data[i,:]-cluster_cen[j,:] 
        distance=np.inner(sub_data,sub_data) 
        if distance<min_distance: 
          min_distance=distance 
          min_index=j+1 
 
      if cat[i]!=min_index: 
        is_change=1 
        cat[i]=min_index 
    for j in range(k): 
      cluster_cen[j]=np.mean(data[cat==(j+1)],axis=0) 
 
  return cat,cluster_cen 
 
 
if __name__=='__main__': 
 
  #generate data 
  cov=[[1,0],[0,1]] 
  mean1=[1,-1] 
  x1=np.random.multivariate_normal(mean1,cov,200) 
 
  mean2=[5.5,-4.5] 
  x2=np.random.multivariate_normal(mean2,cov,200) 
 
  mean3=[1,4] 
  x3=np.random.multivariate_normal(mean3,cov,200) 
 
  mean4=[6,4.5] 
  x4=np.random.multivariate_normal(mean4,cov,200) 
 
  mean5=[9,0.0] 
  x5=np.random.multivariate_normal(mean5,cov,200) 
   
  X=np.vstack((x1,x2,x3,x4,x5)) 
   
  #data distribution 
  fig1=plt.figure(1) 
  p1=plt.scatter(x1[:,0],x1[:,1],marker='o',color='r',label='x1') 
  p2=plt.scatter(x2[:,0],x2[:,1],marker='+',color='m',label='x2') 
  p3=plt.scatter(x3[:,0],x3[:,1],marker='x',color='b',label='x3') 
  p4=plt.scatter(x4[:,0],x4[:,1],marker='*',color='g',label='x4') 
  p5=plt.scatter(x5[:,0],x4[:,1],marker='+',color='y',label='x5') 
  plt.title('original data') 
  plt.legend(loc='upper right') 
   
  cat,cluster_cen=k_means(X,5)    
 
  print 'the number of cluster 1:',sum(cat==1) 
  print 'the number of cluster 2:',sum(cat==2) 
  print 'the number of cluster 3:',sum(cat==3) 
  print 'the number of cluster 4:',sum(cat==4) 
  print 'the number of cluster 5:',sum(cat==5) 
 
   
  fig2=plt.figure(2) 
  for i,m,lo,label in zip(range(5),['o','+','x','*','+'],['r','m','b','g','y'],['x1','x2','x3','x4','x5']): 
 
    p=plt.scatter(X[cat==(i+1),0],X[cat==(i+1),1],marker=m,color=lo,label=label) 
  plt.legend(loc='upper right') 
  plt.title('the clustering result') 
  plt.show()

以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。

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