word linkage 选择合适的聚类个数matlab code

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clear load fisheriris X = meas; m  = size(X,2);  % load machine %  load census % % X = meas; % X=X(1:2000,:); d = pdist(X,'euclidean');   Z = linkage(d,'ward');%Create a hierarchical binary cluster tree using linkage % [Hr,Tr]=dendrogram(Z, 0);  %generates a dendrogram plot of the hierarchical binary cluster tree % h_gca = gca; % h_gca.TickDir = 'out'; % h_gca.TickLength = [.002 0]; % h_gca.XTickLabel = []; % c = cluster   val0=eval(vpa(Z(:,3),5));   t=1; n(1)=0; val=val0(1); f(1)=val; for i=1:length(val0)     if val0(i)==val         n(t)=n(t)+1;         continue     else         t=t+1;         val=val0(i);         n(t)=1;         f(t)=val;         continue     end end figure(2) fsum=cumsum(n); x= length(val0)-fsum; plot(x, f, 'o-','LineWidth', 2) xlabel('Number of Clusters') ylabel('Merge Distance') title('A sample evalution graph')   % for i = 1: length(x) %     y= cluster(Z, x(i)); %     B = Amean(X, y) % end   x =x(end:-1:1); f=f(end:-1:1);   i=0; flag = 1;  b =  length(x); minerror_c_old = b-1; while flag ~=0 & b>=20     RMSE = [];     for c = 2: (b-2)         x1 = x(1:c); y1 = f(1 : c);         x2 = x(c:b); y2 = f(c : b);         [~, s1] = polyfit(x1, y1,1);         [~, s2] = polyfit(x2, y2,1);         RMSE(c-1) = ((c-1)/(b-1))*s1.normr + ( (b-c)/(b-1))*s2.normr;     end      [~, minerror_c ]= min(RMSE);      minerror_c= minerror_c+1;      if  minerror_c >= minerror_c_old          break      else          minerror_c_old = minerror_c; %           b = b -1;           b = 2*minerror_c ;      end      i=i+1 end   hold on plot(x( minerror_c), f( minerror_c),'ro') c =x( minerror_c)   labels = cluster(Z, c);   labs = unique(labels); CORR = zeros(); for i = 1:length(labs)     corri = cov(X(labels == i, :));     CORR = CORR +  corri; end