function kmeansit%h-MEANS! clear;clc;load data;tic;k=5;[n,d]= size(X);%Dialekse kapoies parthrhseis gia ta centroids.ind = randperm(n);ind = ind(1:k);nc = X(ind,:);%8ese thn apo8hkh gia ta melh twn clusters.%Oi akeraioi 1,....,k deixnoun se poio cluster eisai.cid = zeros(1,n);%Kane to  diaforetiko gia na arxisei to loopoldcid = ones(1,n);% O ari8mos se ka8e clusternr = zeros(1,k);%8ese megisto ari8mo apo iterationsmaxiter = 100;iter =1;while ~isequal(cid,oldcid)& iter < maxiter    oldcid = cid;       %Ftiakse ton hmeans algori8mo.   %Gia ka8e shmeio, vres thn norma pou 8es (e8esa eukleidia)   %pros opoiodhpote centroid.      for i=1:n       dist = sum((repmat(X(i,:),k,1)-nc).^2,2);       %tautopoieise to me to cluster k       [m,ind] = min(dist);       cid(i) = ind;   end   %Vres ta nea centroids.   for i = 1:k       %Vres ola ta shmeia se auto to cluster.       ind = find(cid==i);       %Vres & to centroid.       nc(i,:) = mean(X(ind,:));       %Vres kai to numero se ka8e cluster.      nr(i) = length(ind);  end  iter =iter +1enddisp('Time needed for finding "better" initial centroids: '),disp(toc);%Initial partitions found %Proceeding to k-means algorithm given the centroids from the h-means. [cidx, ctrs] = kmeans(X, 5, 'dist','city','start',nc,'rep',1,'disp','final'); plot(X(cidx==1,1),X(cidx==1,2),'r.', ...        X(cidx==2,1),X(cidx==2,2), 'b.', ...        X(cidx==3,1),X(cidx==3,2), 'm.', ...        X(cidx==4,1),X(cidx==4,2), 'y.', ...        X(cidx==5,1),X(cidx==5,2), 'g.', ...        ctrs(1,1),ctrs(1,2),'kx',ctrs(2,1),ctrs(2,2),'kx',...        ctrs(3,1),ctrs(3,2),'kx',ctrs(4,1),ctrs(4,2),'kx',...        ctrs(5,1),ctrs(5,2),'kx');        save M5 cidx