function [dbelong, neach, clusters, niter, ST, DB] = ...    cluster2a_ser(data, nc, np, incnp, eta, deta, maxEpoch, info, psec);%% The size of the datasetindata=data(:,1:2308);target=data(:,2309);[ndata,nvar] = size(indata);class=ones(1,ndata);col=class;leaf=0;if info >= 1  disp(sprintf('Number of datapoints: %d', ndata));  disp(sprintf('Number of variables:  %d', nvar));end% Normalize data to unit standard deviation and zero mean% using prestd.[indata, datamean, datastd] = kmeans_norm(indata); %%%%%%%%%% Parameters for k-means %%%%%%%%%%    npoints = ndata;    clust(1,1)=0;    clust(1,2)=0;CP=zeros(1,200);clust=zeros(1000,nvar);dotter=1000*ones(1000,2);mor=dotter;N=777*ones(1,500);N(1)=200;ant=1;cl=0;go2=1;while leaf<63 & cl<900    cl=cl+1;           % The starting clusters    u=find(class==cl);        if length(u)>=3        rand('state',sum(100*clock));        clusters(1,:) = 0.0001 * (rand(1,nvar) - 0.5)+clust(cl,:);   % Slumpar startpunkter.        clusters(2,:) = 0.0001 * (rand(1,nvar) - 0.5)+clust(cl,:);   % Slumpar startpunkter.        bclusters=clusters;        niter = 0;        go = 1;        qerr_old = 0.0;        cold = zeros(nc,nvar);                  while go == 1            niter = niter + 1;            data = indata;              % Compute the distance between each datapoint to the clusters            dists = kmeans_dist(indata, clusters);                % Assign datapoints to clusters and compute updates            [minval, minidx] = min(dists');            for i=1:nc                idxtmp = find(minidx == i & class==cl);                 if length(idxtmp > 0)                                        cpoints = indata(idxtmp,:);		                % Compute the mean of the indatapoints belonging to cluster i	                if length(idxtmp)==1                        centroid=cpoints;                    else                        centroid = mean(cpoints);                    end                    update(i,:) = centroid - clusters(i,:);                                                    else	                update(i,:) = zeros(1,2);                                                                                                end            end                        % Update the cluster centers            clusters = clusters + eta * update;                update_old=update;                                    % Compute changes            dcluster = sum( sqrt( sum( ( (clusters-cold).*(clusters-cold) )') ) ) / ...            nc;            cold = clusters;            %dcluster = sum( sqrt( sum( (update.*update)') ) ) / nc;            % Compute some clusters statistics            [dbelong, neach, avedist, quanterr] = ...            cluster_stat(indata, clusters, nc,cl,class);              % Some plotting if 2 dimenstions            if nvar == 2 & info > 0 & cl<8                  plot(clusters(:,1), clusters(:,2),'+r');                %cluster_plotclusters(fig, clusters,cl,clust);            end              % Stopping criteria            if abs(quanterr - qerr_old) < 0.00001 | niter >= maxEpoch                 go = 0;            end            qerr_old = quanterr;              % Update the learning rate            %eta = eta * deta;              % This for visualization purposes            %pause(psec);        end                % Final plotting if in 2 dimensions                for i=1:2            idxtmp = find(minidx == i & class==cl);            ant=ant+1;            class(idxtmp)=ant;            N(ant)=length(idxtmp);              dotter(cl,i)=ant;                mor(ant)=cl;        end                            clust(ant-1:ant,:)=clusters;            elseif length(u)==2                ant=ant+1;        clust(ant,:)=indata(u(1),:);        dotter(cl,1)=ant;        mor(ant)=cl;        class(u(1))=ant;        N(ant)=1;        ant=ant+1;        clust(ant,:)=indata(u(2),:);        dotter(cl,2)=ant;        mor(ant)=cl;        class(u(2))=ant;        N(ant)=1;            elseif length(u)==1        CP(u)=2;        leaf =leaf+1;                floor(100*leaf/63)            endendfor i=1:4    tmp=find(target==i);    j=class(tmp);    ccol(j)=i;end                g=ones(1,1000);y=zeros(1,1000);x=y;for i=1:cl    d=dotter(i,:);    if d(1)<1000                g(d(1))=g(i)+1;        x(d(1))=x(i)+.5^g(i);        g(d(2))=g(i)+1;        x(d(2))=x(i)-.5^g(i);    endendu=find(N(1:cl)==1);[tmp, q]=sort(x(u));z=u(q);y(z)=1:length(z);[tmp, j]=sort(N(1:cl));figurehold onfor i=1:length(j)    s=j(i);    d=dotter(s,:);    if d(1)<1000        y(s)=(y(d(1))+y(d(2)))/2;        plot ([g(d(1)), g(s), g(s), g(d(2))], [y(d(1)), y(d(1)), y(d(2)), y(d(2))],'k')        if ccol(d(1)) == ccol(d(2))            ccol(s)=ccol(d(1));        end    endend    plot ([g(1)-1,g(1)],[y(1),y(1)],'k')    for i=1:cl    if ccol(i)==1        plot ([g(i)-1,g(i)],[y(i),y(i)],'m')    end    if ccol(i)==2        plot ([g(i)-1,g(i)],[y(i),y(i)],'g')    end    if ccol(i)==3        plot ([g(i)-1,g(i)],[y(i),y(i)],'r')    end    if ccol(i)==4        plot ([g(i)-1,g(i)],[y(i),y(i)],'b')    endendaxis ([0 max(g) -5 70])