clustering的经典k-mean算法源程序，matlab

function y=kMeansCluster(m,k,isRand) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % kMeansCluster - Simple k means clustering algorithm % % Author: Kardi Teknomo, Ph.D. % % % % Purpose: classify the objects in data matrix based on the attributes % % Criteria: minimize Euclidean distance between centroids and object points % % For more explanation of the algorithm, see http://people.revoledu.com/kardi/tutorial/kMean/index.html % % Output: matrix data plus an additional column represent the group of each object % % % % Example: m = [ 1 1; 2 1; 4 3; 5 4] or in a nice form % % m = [ 1 1; % % 2 1; % % 4 3; % % 5 4] % % k = 2 % % kMeansCluster(m,k) produces m = [ 1 1 1; % % 2 1 1; % % 4 3 2; % % 5 4 2] % % Input: % m - required, matrix data: objects in rows and attributes in columns % % k - optional, number of groups (default = 1) % isRand - optional, if using random initialization isRand=1, otherwise input any number (default) % it will assign the first k data as initial centroids % % Local Variables % f - row number of data that belong to group i % c - centroid coordinate size (1:k, 1:maxCol) % g - current iteration group matrix size (1:maxRow) % i - scalar iterator % maxCol - scalar number of rows in the data matrix m = number of attributes % maxRow - scalar number of columns in the data matrix m = number of objects % temp - previous iteration group matrix size (1:maxRow) % z - minimum value (not needed) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if nargin<3, isRand=0; end if nargin<2, k=1; end [maxRow, maxCol]=size(m) if maxRow<=k, y=[m, 1:maxRow] else % initial value of centroid if isRand, p = randperm(size(m,1)); % random initialization for i=1:k c(i,:)=m(p(i),:) end else for i=1:k c(i,:)=m(i,:) % sequential initialization end end temp=zeros(maxRow,1); % initialize as zero vector while 1, d=DistMatrix(m,c); % calculate objcets-centroid distances [z,g]=min(d,[],2); % find group matrix g if g==temp, break; % stop the iteration else temp=g; % copy group matrix to temporary variable end for i=1:k f=find(g==i); if f % only compute centroid if f is not empty c(i,:)=mean(m(find(g==i),:),1) end end end y=[m,g]; end