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)
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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
