function [g, c, tmp, m]=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
tmp = [];
[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
%The Matlab function kMeansCluster above call function DistMatrix as shown in the code below. It works for multi-dimensional Euclidean distance. Learn about other type of distance here.
function d=DistMatrix(A,B)
%%%%%%%%%%%%%%%%%%%%%%%%%
% DISTMATRIX return distance matrix between points in A=[x1 y1 ... w1] and in B=[x2 y2 ... w2]
% Copyright (c) 2005 by Kardi Teknomo, http://people.revoledu.com/kardi/
%
% 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);
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