1、库

import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

2、随机生成聚类中心点

def initCentroids(dataSet,k):
    numSamples,dim=dataSet.shape
    centroids=np.zeros((k,dim))
    for i in range(k):
        index=int(np.random.uniform(0,numSamples))
        centroids[i,:]=dataSet[index,:]
    return centroids

3、欧氏距离

def euclDistance(vector1,vector2):
    return np.sqrt(np.sum(np.power(vector2-vector1,2)))

4、K-均值聚类

def kmeans(dataSet,k):
    numSamples=dataSet.shape[0]
    clusterAssment=np.mat(np.zeros((numSamples,2)))
    clusterChanged=True
    centroids=initCentroids(dataSet,k)
    while clusterChanged:
        clusterChanged=False
        for i in range(numSamples):
            minDist=100000.0
            minIndex=0
            for j in range(k):
                distance=euclDistance(centroids[j,:],dataSet[i,:])
                if distance<minDist:
                    minDist=distance 
                    minIndex=j 
            if clusterAssment[i,0]!=minIndex: 
                clusterChanged=True
                clusterAssment[i,:]=minIndex,minDist**2
        for j in range(k):
            pointsInCluster=dataSet[np.nonzero(clusterAssment[:,0].A==j)[0]]
            centroids[j,:]=np.mean(pointsInCluster,axis=0)
    print('KNN聚类完成！')
    return centroids,clusterAssment

5、二维平面显示聚类结果

def showCluster(dataSet,k,centroids,clusterAssment):
    fig_2d_clustered=plt.figure()
    ax2d_clustered=fig_2d_clustered.add_subplot(111)
    
    numSamples,dim=dataSet.shape
    if dim!=2:
        print("只能绘制二维图形：")
        return 1
    mark=['.r','+b','*g','lk','^r','vr','sr','dr','<r','pr']
    if k>len(mark):
        print("K值过大！")
        return 1
    for i in range(numSamples):
        markIndex=int(clusterAssment[i,0])
        ax2d_clustered.plot(dataSet[i,0],dataSet[i,1],mark[markIndex])
    for i in range(k):
        ax2d_clustered.plot(centroids[i,0],centroids[i,1],mark[i],markersize=20)
    fig_2d_clustered.savefig('clusterRes.png',dpi=300,bbox_inches='tight')
    fig_2d_clustered.show()

6、结果

#调用以上函数，对读入数据进行聚类
print("step1:读入数据：")
dataSetKNN=[]
fileIn=open('testSet.txt')
for line in fileIn.readlines():
    lineArr=line.strip().split(' ') 
    dataSetKNN.append([float(lineArr[0]),float(lineArr[1])])
dataSetKNNSize=len(dataSetKNN) 
dataSetKNN1=np.mat(dataSetKNN)
for i in range(dataSetKNNSize):
    plt.plot(dataSetKNN1[i,0],dataSetKNN1[i,1],'b*')
print("原始数据分布：")
plt.savefig('ch12_knn_orig.png',dpi=300,bbox_inches='tight')
plt.show()

#K 取值4，调用K均值算法聚类
print("step2:聚类")
k=4
centroids,clusterAssment=kmeans(dataSetKNN1,k)
print('数据类型：',dataSetKNN1.dtype)
print("step3:结果输出：")
showCluster(dataSetKNN1,k,centroids,clusterAssment)

step1:读入数据：
原始数据分布：
step2:聚类
KNN聚类完成！
数据类型： float64
step3:结果输出：
C:\Users\86186\Desktop\2021-2022（2）课程\2021-2022（2）Python金融数据挖掘\课件\第11章\例11-2.py:94: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig_2d_clustered.show()