distmatrix.m

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters S

function d=DistMatrix(A,B)
%--------------------------------------------------------------------------
% DISTMATRIX return distance matrix between points in A=[x1 y1 ... w1] and in B=[x2 y2 ... w2]
%
% Numbers of rows (represent points) in A and B are not necessarily the same.
% It can be use for distance-in-a-slice (Spacing) or distance-between-slice (Headway),
%
% A and B must contain the same number of columns (represent variables of n dimensions),
% first column is the X coordinates, second column is the Y coordinates, and so on.
% The distance matrix is distance between points in A as rows
% and points in B as columns.
% example: Spacing= dist(A,A)
% Headway = dist(A,B), with hA ~= hB or hA=hB
%          A=[1 2 3; 4 5 6; 2 4 6; 1 2 3]; B=[4 5 1; 6 2 0]
%          dist(A,B)= [ 4.69   5.83;
%                       5.00   7.00;
%                       5.48   7.48;
%                       4.69   5.83 ];
%
%          dist(B,A)= [ 4.69   5.00     5.48    4.69;
%                       5.83   7.00     7.48    5.83 ];
%--------------------------------------------------------------------------

[hA,wA]=size(A);             
[hB,wB]=size(B);             
if wA ~= wB,  
    error('second dimension of A and B must be the same'); 
end             
for k=1:wA                  
    C{k}= repmat(A(:,k),1,hB);                  
    D{k}= repmat(B(:,k),1,hA);             
end             
S=zeros(hA,hB);             
for k=1:wA                  
    S=S+(C{k}-D{k}').^2;             
end             
d=sqrt(S);