kmeanscluster.m

K-均值聚类

function [y,c]=kMeansCluster(m,k,isRand)
%-------------------------------------------------------------
%                                                        
% kMeansCluster - Simple k means clustering algorithm                                                                                                                               
%                                                                                                                    
% 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