Matlab comes with K-means clustering ‘out of the box’. The GNU Octave work-a-like system doesn’t, and there seem to be quite a few implementations floating around. I picked the first from Google, pretty carelessly, saving as myKmeans.m. These are notes from trying to reproduce this Matlab demo with Octave. Not rocket science but worth writing down so I can find it again.

M=4
W=2
H=4
S=500
a = M * [randn(S,1)+W, randn(S,1)+H];
b = M * [randn(S,1)+W, randn(S,1)-H];
c = M * [randn(S,1)-W, randn(S,1)+H];
d = M * [randn(S,1)-W, randn(S,1)-H];
e = M * [randn(S,1), randn(S,1)];
all_data = [a;b;c;d;e];
plot(a(:,1), a(:,2),'.');
hold on;
plot(b(:,1), b(:,2),'r.');
plot(c(:,1), c(:,2),'g.');
plot(d(:,1), d(:,2),'k.');
plot(e(:,1), e(:,2),'c.');
% using http://www.christianherta.de/kmeans.html as myKmeans.m
[centroid,pointsInCluster,assignment] = myKmeans(all_data,5)
scatter(centroid(:,1),centroid(:,2),'x');