kmeans算法分类实例_kmeas算法_matlab_仿真实例

%% kMeans的核心程序，不断迭代求解聚类中心 function [ centroids ] = kMeans( A, k ) [m,n] = size(A); %初始化聚类中心 centroids = zeros(k, n); global subCenter; subCenter = zeros(m,2);%做一个m*2的矩阵，第一列存储类别，第二列存储距离 %求取聚类中心 for j = 1:n minJ = min(A(:,j)); rangeJ = max(A(:,j))-min(A(:,j)); centroids(:,j) = minJ+rand(k,1)*rangeJ;%产生区间上的随机数 end change = 1;%判断是否改变 while change == 1 change = 0; %对每一组数据计算距离 for i = 1:m minDist = inf; minIndex = 0; for j = 1:k %计算欧氏距离 dist=(A(i,:)-centroids(j,:))*(A(i,:)-centroids(j,:))'; if dist < minDist minDist = dist; minIndex = j; end end if subCenter(i,1) ~= minIndex change = 1; subCenter(i,:)=[minIndex, minDist]; end end %对k类重新就算聚类中心 for j = 1:k sum = zeros(1,n); r = 0;%数量 for i = 1:m if subCenter(i,1) == j sum = sum + A(i,:); r = r+1; end end centroids(j,:) = sum./r; end end