Added plot_dendrogram function for Hierarchical Clustering

Visualizing the hierarchy of the resulting hierarchical clustering can be useful.

This pull request adds the plot_dendrogram function within sklearn.cluster.hierarchical. This function makes good use of scipy's [dendrogram function]( http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html).

An example use case:

| Code  | Result |
| ------------- | ------------- |
|  `import pylab as plt`<br>`from sklearn.datasets import load_iris`<br>` `<br> `from sklearn.cluster import AgglomerativeClustering`<br>`from sklearn.cluster.hierarchical import plot_dendrogram `<br>` `<br>`model = AgglomerativeClustering()`<br>`iris = load_iris()`<br>`model = model.fit(iris.data[:20])`<br>` plot_dendrogram(model)`<br>`plt.show()`| <img src="http://oi59.tinypic.com/vie8es.jpg" width="200px" height="200px" />  |

Please let me know if you find this useful, or if I should change anything!

Thanks!,
Mat
This should rather be a **kwargs passed to the dendrogram function, I think. And it should be well documented that all threshold-based parameters will not use real distances, but each merging step is assumed to be at a distance of 1 (if I read correctly).