[MRG+1] Clustering algorithm - BIRCH 

Fixes https://github.com/scikit-learn/scikit-learn/issues/2690

The design is similar to the Java code written here https://code.google.com/p/jbirch/
I am pretty much sure it works (If the JAVA implementation is correct, ofc), since I get the same clusters for both cases. I opened this as a Proof of Concept.

This example has been modified, http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html
 
When `threshold` is set to 3.0

![figure_1](https://cloud.githubusercontent.com/assets/1867024/4772737/1dd8604a-5b9b-11e4-800d-aea031410a30.png)

When `threshold` is set to 1.0

![figure_2](https://cloud.githubusercontent.com/assets/1867024/4772748/2e46daec-5b9b-11e4-8f49-672b0ada17b7.png)

TODO: A LOT

- [x] Make it as fast as possible.
- [x] Support for sparse matrices.
- [x] Extensive testing.
- [x] Make the common tests pass, for now I have added it to `dont_test` ;)
- [x] Narrative documentation

Awating some initial feedback!

 
> And this should only be true for euclidean metrics, I think. I'm not really
> sure what the default tolerance should be.

One typical way of computing such parameter is to take a given percentile
of the distribution of pairwise distances (I believe that there is an
option to do that in our AffinityPropagation).

Of course, on an on-line method, we wouldn't want to require the whole
dataset to compute the pairwise distances, but we can use the first
batch, with the hope that it is representative.