1) Randomly initialize 10 cluster centroids. This can be done by simply randomly selecting 10 points from your dataset.
2) Compute the distance (Euclidean, presumably) from each data point to these 10 centroids.
3) Assign cluster membership of each point to the cluster who's centroid is the closest.
4) Re-compute centroid of each cluster
5) Compute distance from each data point to the 10 centroids.
6) So on...
Plotting:
for i=1:10
plot(matrix(cluster==i,dim1),matrix(cluster==i,dim2),'o')
hold on
end
In this plot, you have to choose two dimensions to plot against each other. From the looks of it, you have either 256 or 1707 dimensions (aka features).
