kmeans-vector

Provides a simple (but efficient) implementation of the k-means clustering algorithm. The goal of this algorithm is to, given a set of n-dimensional points, regroup them in k groups, such that each point gets to be in the group to which it is the closest to (using the center of the group). CHANGELOG 0.3: total rewrite of the code, the code scales much better on big inputs and is overall consistently faster than the other kmeans implementations on hackage, on my laptop. 0.2: supports having feature vectors associated to objects, and thus computing kmeans on these vectors, letting you recover the initial objects.