聚类分析matlab_matlab聚类分析代码_分类_

clc; clear; close all; data = [0.697 0.460;0.774,0.376;0.634,0.264;0.608,0.318;0.556,0.215;0.403,0.237; 0.481,0.149;0.437,0.211;0.666,0.091;0.243,0.267;0.245,0.057;0.343,0.099; 0.639 0.161;0.657,0.198;0.360,0.370;0.593,0.042;0.719,0.103;0.359,0.188; 0.339,0.241;0.282,0.257;0.748,0.232;0.714,0.346;0.483,0.312;0.478,0.437; 0.525,0.369;0.751,0.489;0.532,0.472;0.473,0.376;0.725,0.445;0.446,0.459;] %聚类 数值初始化 % scatter(data(:,1),data(:,2)) % axis([0.1 0.9 0 0.8]) num = length(data); %样本数 fea_n=2; %特征数 k = 3; %簇数 %如果各特征数值差异较大，需要归一化 % maxd = max(data,2); % data(:,1:fea_n)=data(:,1:fea_n)./repmat(maxd(1,1:fea_n),num,1); % totalMean = mean(data(:,1:fea_n)); %产生不相等随机数 % b = randperm(num); % r = b(1:k); r = [6 12 24];%书中随机选取的初始化质心 %初始均值向量 u 1行 * k*fea_n列 u = [data(r(1,1),1:fea_n)]; for n = 2:k u = [u , data(r(1,n),1:fea_n)]; end count = 1;%迭代次数 flag = 1; cellshape([1:k],1) = fea_n; while(flag&count<=4) if count == 1 %第迭代循环初始化 old_u = u; end difer = (repmat(data(:,1:fea_n),1,k) - repmat(old_u,num,1)); cell = mat2cell((difer.*difer),[num],cellshape); %按簇划分子块 distcell = cell2mat(cellfun(@(x) sum(x,2).^0.5, cell,'UniformOutput',false)'); %计算距离 dist=reshape(distcell,[],k); [mini,lambda] = min(dist,[],2); %找到最近似的矩阵 data = [data(:,1:fea_n),lambda]; %标记数据 data = sortrows(data,3); %各簇内记录的数量 for n = 1:k len(1,n) = length(find(lambda==n)); end datacell = mat2cell(data(:,1:fea_n)',[fea_n],len); new_u = cell2mat(cellfun(@(x) mean(x,2),datacell,'UniformOutput',false)')'; flag = length(find(new_u-old_u~=0)); old_u = new_u; count=count+1; end %画出分类结果 Color_map = hsv(k); Label = data(:,fea_n+1); Color_Label = Color_map(Label,:); figure(1),clf C=reshape(new_u',2,[])'; plot(C(:,1),C(:,2),'kx','MarkerSize',10,'LineWidth',3) hold on; scatter(data(:,1),data(:,2),40,Color_Label,'filled') xlabel('密度') ylabel('含糖率') title('第四轮迭代后') rng('default'); % For reproducibility eva = evalclusters(data,'kmeans','CalinskiHarabasz','KList',[1:10]) plot(eva)