This procedure is a variant of k-means using labelled samples, which enforces in every cluster the same proportion of samples from every class. This ensures that the resulting clusters are totally non-informative about the class, while maximally informative about the signal.
You can get a short report on the method,.
Executing
./test.shwill compile the source, run the algorithm on a 2d toy example, and produce three graphs (result-standard.png, result-clueless.png, and result-clueless-absolute.png) if you have gnuplot installed.
