When I am running following K-means code ... it gives me an error as "Normalization....." Please help me how to run this code.

% K-means clustering % ------------------- CLUSTERING PHASE ------------------- % Load the Training Set** TrSet = load('TrainingSet.txt'); [m,n] = size(TrSet); % (m samples) x (n dimensions) for i = 1:m % the output (last column) values (0,1,2,3) are mapped to (0,1) if TrSet(i,end)>=1 TrSet(i,end)=1; end end % find the range of each attribute (for normalization later) for i = 1:n range(1,i) = min(TrSet(:,i)); range(2,i) = max(TrSet(:,i)); end x = Normalize(TrSet, range); % normalize the data set to a hypercube x(:,end) = []; % get rid of the output column [m,n] = size(x); nc = 2; % number of clusters = 2 % Initialize cluster centers to random points c = zeros(nc,n); for i = 1:nc rnd = int16(rand*m + 1); % select a random vector from the input set c(i,:) = x(rnd,:); % assign this vector value to cluster (i) end % Clustering Loop delta = 1e-5; n = 1000; iter = 1; while (iter < n) % Determine the membership matrix U % u(i,j) = 1 if euc_dist(x(j),c(i)) <= euc_dist(x(j),c(k)) for each k ~= i % u(i,j) = 0 otherwise for i = 1:nc for j = 1:m d = euc_dist(x(j,:),c(i,:)); u(i,j) = 1; for k = 1:nc if k~=i if euc_dist(x(j,:),c(k,:)) < d u(i,j) = 0; end end end end end % Compute the cost function J J(iter) = 0; for i = 1:nc JJ(i) = 0; for k = 1:m if u(i,k)==1 JJ(i) = JJ(i) + euc_dist(x(k,:),c(i,:)); end end J(iter) = J(iter) + JJ(i); end

% Stop if either J is below a certain tolerance value, % or its improvement over previous iteration is below a certain threshold str = sprintf('iteration: %.0d, J=%d', iter, J(iter)); disp(str); if (iter~=1) & (abs(J(iter-1) - J(iter)) < delta) break; end % Update the cluster centers % c(i) = mean of all vectors belonging to cluster (i) for i = 1:nc sum_x = 0; G(i) = sum(u(i,:)); for k = 1:m if u(i,k)==1 sum_x = sum_x + x(k,:); end end c(i,:) = sum_x ./ G(i); end iter = iter + 1; end % while disp('Clustering Done.'); % ----------------- TESTING PHASE -------------------------- % Load the evaluation data set EvalSet = load('EvaluationSet.txt'); [m,n] = size(EvalSet); for i = 1:m if EvalSet(i,end)>=1 EvalSet(i,end)=1; end end x = Normalize(EvalSet, range); x(:,end) = []; [m,n] = size(x); % Assign evaluation vectors to their respective clusters according % to their distance from the cluster centers for i = 1:nc for j = 1:m d = euc_dist(x(j,:),c(i,:)); evu(i,j) = 1; for k = 1:nc if k~=i if euc_dist(x(j,:),c(k,:)) < d evu(i,j) = 0; end end end end end % Analyze results ev = EvalSet(:,end)'; rmse(1) = norm(evu(1,:)-ev)/sqrt(length(evu(1,:))); rmse(2) = norm(evu(2,:)-ev)/sqrt(length(evu(2,:))); subplot(2,1,1); if rmse(1) < rmse(2) r = 1; else r = 2; end str = sprintf('Testing Set RMSE: %f', rmse(r)); disp(str); ctr = 0; for i = 1:m if evu(r,i)==ev(i) ctr = ctr + 1; end end str = sprintf('Testing Set accuracy: %.2f%%', ctr*100/m); disp(str); [m,b,r] = postreg(evu(r,:),ev); % Regression Analysis disp(sprintf('r = %.3f', r));