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+# Introduction
+
+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,`](https://fleuret.org/papers/fleuret-clueless-kmeans2015.pdf).
+
+# Installation
+
+Executing
+
+```
+./test.sh
+```
+
+will compile the source, run the algorithm on a 2d toy example, and
+produce three graphs
+([`result-standard.png,`](https://fleuret.org/git-extract/clueless-kmeans/result-standard.png)
+[`result-clueless.png,`](https://fleuret.org/git-extract/clueless-kmeans/result-clueless.png)
+and [`result-clueless-absolute.png`](https://fleuret.org/git-extract/clueless-kmeans/result-clueless-absolute.png)) if you
+have [`gnuplot`](http://www.gnuplot.info/) installed.