k-means.rar_K-Means聚类_K._k-means_k-means 聚类_say7s2

clear all;close all;clc; mu1=[0 0 ]; S1=[.1 0 ;0 .1]; data1=mvnrnd(mu1,S1,100); mu2=[1.25 1.25 ]; S2=[.1 0 ;0 .1]; data2=mvnrnd(mu2,S2,100); mu3=[-1.25 1.25 ]; S3=[.1 0 ;0 .1]; data3=mvnrnd(mu3,S3,100); % 显示数据 plot(data1(:,1),data1(:,2),'b+'); hold on; plot(data2(:,1),data2(:,2),'r+'); plot(data3(:,1),data3(:,2),'g+'); grid on; % 三类数据合成一个不带标号的数据类 data=[data1;data2;data3]; N=3;%设置聚类数目 [m,n]=size(data); pattern=zeros(m,n+1); center=zeros(N,n);%初始化聚类中心 pattern(:,1:n)=data(:,:); for x=1:N center(x,:)=data( randi(300,1),:);%第一次随机产生聚类中心 end while 1 distence=zeros(1,N); num=zeros(1,N); new_center=zeros(N,n); for x=1:m for y=1:N distence(y)=norm(data(x,:)-center(y,:)); %计算到每个类的距离 end [~, temp]=min(distence);%求最小的距离 pattern(x,n+1)=temp; end k=0; %更新聚类中心 for y=1:N for x=1:m if pattern(x,n+1)==y new_center(y,:)=new_center(y,:)+pattern(x,1:n); num(y)=num(y)+1; end end new_center(y,:)=new_center(y,:)/num(y); if norm(new_center(y,:)-center(y,:))<0.1 k=k+1; end end if k==N break; else center=new_center; end end [m, n]=size(pattern); %最后显示聚类后的数据 figure; hold on; for i=1:m if pattern(i,n)==1 plot(pattern(i,1),pattern(i,2),'r*'); plot(center(1,1),center(1,2),'ko'); elseif pattern(i,n)==2 plot(pattern(i,1),pattern(i,2),'g*'); plot(center(2,1),center(2,2),'ko'); elseif pattern(i,n)==3 plot(pattern(i,1),pattern(i,2),'b*'); plot(center(3,1),center(3,2),'ko'); elseif pattern(i,n)==4 plot(pattern(i,1),pattern(i,2),'y*'); plot(center(4,1),center(4,2),'ko'); else plot(pattern(i,1),pattern(i,2),'m*'); plot(center(4,1),center(4,2),'ko'); end end grid on;