cskmeans.zip_KMEANS MATLAB_matlab数据聚类_数据聚类_聚类算法

function [cid,nr,centers] = cskmeans(x,k,nc) % CSKMEANS K-Means clustering - general method. % % This implements the more general k-means algorithm, where % HMEANS is used to find the initial partition and then each % observation is examined for further improvements in minimizing % the within-group sum of squares. % % [CID,NR,CENTERS] = CSKMEANS(X,K,NC) Performs K-means % clustering using the data given in X. % % INPUTS: X is the n x d matrix of data, % where each row indicates an observation. K indicates % the number of desired clusters. NC is a k x d matrix for the % initial cluster centers. If NC is not specified, then the % centers will be randomly chosen from the observations. % % OUTPUTS: CID provides a set of n indexes indicating cluster % membership for each point. NR is the number of observations % in each cluster. CENTERS is a matrix, where each row % corresponds to a cluster center. % % See also CSHMEANS % W. L. and A. R. Martinez, 9/15/01 % Computational Statistics Toolbox warning off [n,d] = size(x); if nargin < 3 % Then pick some observations to be the cluster centers. ind = ceil(n*rand(1,k)); % We will add some noise to make it interesting. nc = x(ind,:) + randn(k,d); end % set up storage % integer 1,...,k indicating cluster membership cid = zeros(1,n); % Make this different to get the loop started. oldcid = ones(1,n); % The number in each cluster. nr = zeros(1,k); % Set up maximum number of iterations. maxiter = 100; iter = 1; while ~isequal(cid,oldcid) & iter < maxiter % Implement the hmeans algorithm % For each point, find the distance to all cluster centers for i = 1:n dist = sum((repmat(x(i,:),k,1)-nc).^2,2); [m,ind] = min(dist); % assign it to this cluster center cid(i) = ind; end % Find the new cluster centers for i = 1:k % find all points in this cluster ind = find(cid==i); % find the centroid nc(i,:) = mean(x(ind,:)); % Find the number in each cluster; nr(i) = length(ind); end iter = iter + 1; end % Now check each observation to see if the error can be minimized some more. % Loop through all points. maxiter = 2; iter = 1; move = 1; while iter < maxiter & move ~= 0 move = 0; % Loop through all points. for i = 1:n % find the distance to all cluster centers dist = sum((repmat(x(i,:),k,1)-nc).^2,2); r = cid(i); % This is the cluster id for x %%nr,nr+1; dadj = nr./(nr+1).*dist'; % All adjusted distances [m,ind] = min(dadj); % minimum should be the cluster it belongs to if ind ~= r % if not, then move x cid(i) = ind; ic = find(cid == ind); nc(ind,:) = mean(x(ic,:)); move = 1; end end iter = iter+1; end centers = nc; if move == 0 disp('No points were moved after the initial clustering procedure.') else disp('Some points were moved after the initial clustering procedure.') end warning on