聚类分析 Python 自定义K-means函数（学习笔记）

from numpy import *
import matplotlib.pyplot as plt
from math import sqrt

#距离度量函数（欧氏距离）
def eucDistance(vec1,vec2):
    return sqrt(sum(power(vec2-vec1,2)))

#初始聚类中心选择
def initCentroids(dataSet,k):
    numSamples,dim=dataSet.shape
    centroids=zeros((k,dim))
    for i in range(k):
        index=int(random.uniform(0,numSamples))
        centroids[i,:]=dataSet[index,:]
    return centroids

#K-means聚类算法
#创建K个质心，再将每个数据点分配到最近的质心，然后重新计算质心
def kmeanss(dataSet,k):
    numSamples=dataSet.shape[0]
    clusterAssement=mat(zeros((numSamples,2)))
    clusterChange=True
    
    centroids=initCentroids(dataSet,k)#创建K个质心
    
    while clusterChange:#将每个数据点分配到最近的质心
        clusterChange=False
        for i in range(numSamples):
            minDist=100000.0
            minIndex=0
            for j in range(k):
                distance=eucDistance(centroids[j,:],dataSet[i,:])
                if distance<minDist:
                    minDist=distance
                    minIndex=j
            clusterAssement[i,:]=minIndex,minDist**2
            if clusterAssement[i,0]!=minIndex:
                clusterChange=True
                
        for j in range(k):#重新计算质心
            pointsInCluster=dataSet[nonzero(clusterAssement[:,0].A==j)[0]]#第j类的所有不等于0的点,np.nonzero()非零元素的位置
            centroids[j,:]=mean(pointsInCluster,axis=0)#均值为新质心
            
    return centroids,clusterAssement

#聚类结果显示
def showCluster(dataSet,k,centroids,clusterAssement):
    numSamples,dim=dataSet.shape
    mark=['or','ob','og','ok','^r','+r','<r','pr']
    if k>len(mark):
        print('sorry')
        return 1
    for i in range(numSamples):
        markIndex=int(clusterAssement[i,0])
        plt.plot(dataSet[i,0],dataSet[i,1],mark[markIndex],markersize=8)
    mark=['Dr','Db','Dg','Dk','^b','+b','<b','pb']
    for j in range(k):
        plt.plot(centroids[j,0],centroids[j,1],mark[j],markersize=12)
    plt.show()

#导入数据
dataSet=[]
fileIn=open('/testSet.txt')
for line in fileIn.readlines():
    lineArr=line.strip().split('\t')#strip().split('\t')移除首尾的\t
    dataSet.append([float(lineArr[0]),float(lineArr[1])])
    
dataSet=mat(dataSet)
k=4
centroids,clusterAssement=kmeanss(dataSet,k)
    
showCluster(dataSet,k,centroids,clusterAssement)    

来源：CSDN

作者：Monica_Zzz

链接：https://blog.csdn.net/weixin_44819497/article/details/103826307