matlab下kmeans及pam算法对球型数据分类练习

Posted 2020-07-04 风靡oopp

clear all;
clc;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%数据初始化
Data=zeros(3,20000);
%加噪声
for i=1:4000
    Data(1,i)=200;
    Data(2,i)=200;
    Data(3,i)=200;
end
for i=4001:12000
    p=unifrnd(0,50);
    a=unifrnd(0,2*pi);
    b=unifrnd(0,pi);
    Data(1,i)=p*sin(a)*cos(b);
    Data(2,i)=p*sin(a)*sin(b);
    Data(3,i)=p*cos(a);
end
for i=12001:20000
    p=unifrnd(50,100);
    a=unifrnd(0,2*pi);
    b=unifrnd(0,pi);
    Data(1,i)=p*sin(a)*cos(b);
    Data(2,i)=p*sin(a)*sin(b);
    Data(3,i)=p*cos(a);
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%样本数量
[d,N]=size(Data);
%聚类的数目
K=2;
%方法选择
method=‘kmeans‘;
%method=‘kmedoids‘;
%选取初始点
%max_Initial=max(20,N/(5*K));
max_Initial=20;

label=zeros(max_Initial,N);
center=zeros(d,K,max_Initial);
C=zeros(1,N);

%主循环
for initial_Case=1:max_Initial
    pointK=Initial_center(Data,K); 
    iter=0;
    max_iter=1e+3;
    % xK = pointK;
    disp([‘------------KM进行第 ‘ num2str(initial_Case) ‘ 次重新选择初始中心-----------‘])
    %%每次初始化K个中心点后，进行的循环
    while iter < max_iter
        iter = iter+1;
        if mod(iter,50)==0
            disp([‘ 内部循环进行第 ‘ num2str(iter) ‘ 次迭代‘])
        end
        %%%根据数据矩阵P中每个点到中心点的距离(最小)确定所属分类
        for i=1:N
            dert = repmat(Data(:,i),1,K)-pointK;
            distK=sqrt(diag(dert‘*dert));
            [~,j] = min(distK);
            C(i) = j;
        end
        %%%重新计算K个中心点
        xK_=zeros(d,K);
        for i=1:K
            Pi=Data(:,find(C==i));
            Nk = size(Pi,2);
            % K-Means K-Medoids唯一不同的地方：选择中心点的方式
            switch lower(method)
                case ‘kmeans‘ 
                    xK_(:,i) = sum(Pi,2)/Nk;
                case ‘kmedoids‘
                    Dx2 = zeros(1,Nk);
                    for t=1:Nk
                        dx=Pi-Pi(:,t)*ones(1,Nk);
                        Dx2(t)=sum(sqrt(sum(dx.*dx,1)),2);
                    end
                    [~,min_ind] = min(Dx2);
                    xK_(:,i) = Pi(:,min_ind);
                otherwise
                    errordlg(‘请输入正确的方法：kmeans-OR-kmedoids‘,‘MATLAB error‘);
            end
        end
        %判断是否达到结束条件
        if xK_==pointK % & iter>50
            disp([‘###迭代 ‘ num2str(iter) ‘ 次得到收敛的解‘])
            label(initial_Case,:) = C;
            center(:,:,initial_Case) = xK_;
            % plot_Graph(C);
            break;
        end
        pointK=xK_;
        %xK = xK_;
    end
    if iter == max_iter
        disp(‘###达到内部最大迭代次数1000,未得到收敛的解‘);
        label(initial_Case,:) = C;
        center(:,:,initial_Case) = xK_;
        %plot_Graph(C);
        %break
    end
end

%%%%增加对聚类结果最优性的比较 
%距离差
dist_N = zeros(max_Initial,K);
for initial_Case=1:max_Initial
    for k=1:K
        tem=find(label(initial_Case,:)==k);
        dx=Data(:,tem)-center(:,k,initial_Case)*ones(1,size(tem,2));
        dxk=sqrt(sum(dx.*dx,1));
        dist_N(initial_Case,k)=sum(dxk);
        %dist_N(initial_Case,k)=dxk;
    end
end
 
%%%%对于max_Initial次初始化中心点得到的分类错误
%%%%取错误最小的情况的Label作为最终分类
%求K类总的误差
dist_N_sum=sum(dist_N,2);
[distmin,best_ind]=min(dist_N_sum);
%最佳分组
best_Label=label(best_ind,:);
%最佳中心
best_Center=center(:,:,best_ind);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%三维散布图
figure();
scatter3(Data(1,:),Data(2,:),Data(3,:),‘filled‘,‘cdata‘,best_Label);
title(‘Data Distribution‘);