Add a "kmeans" strategy to select the prototypes

[GaelVaroquaux]

Using a kmeans to define prototypes is useful to have a reduced dimensionality. The steps are the following:

1. hash all the strings of the categories
2. random project them in 256 dimensions
3. run a kmeans on the resulting data with number of clusters the desired dimensionality (and n_init=1)
4. use a nearest neighbor from scikit-learn to assign the cluster centers to original categories.

[GaelVaroquaux]

This should probably be implemented in a separate function, as it is well suited for functions.

[GaelVaroquaux]

Closing as implemented.