I won't get enough time to work on this for the next couple of weeks.
We can leave this sit, merge l it as is (given that it could be overridden
by the upcoming hierarchical clustering PR anyway) or someone else could
submit the improved algorithm.
Happy for any of those options.


On 20 September 2013 09:21, Jake Vanderplas <notifications@github.com>wrote:

> In sklearn/cluster/single_linkage.py:
>
> > +    -----
> > +    See examples/plot_single_linkage.py for a visualisation of the threshold
> > +    cutting algorithm on a small dataset.
> > +
> > +    See examples/plot_eac.py for an example. The Evidence Accumulation
> > +    Clustering (EAC) algorithm uses this clusterer in its final clustering
> > +    step.
> > +
> > +    References
> > +    ----------
> > +    Fred, Ana LN, and Anil K. Jain. "Data clustering using evidence
> > +    accumulation." Pattern Recognition, 2002. Proceedings. 16th International
> > +    Conference on. Vol. 4. IEEE, 2002.
> > +    """
> > +    X = atleast2d_or_csr(X)
> > +    X = pairwise_distances(X, metric=metric)
>
> I think the best solution would be to build the graph over the edges given
> by scipy's QHULL wrapper. Then you get a true MST in order NlogN. From my
> recollection, though, I think the qhull wrapper in scipy doesn't provide
> direct access to the edges...
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/1830/files#r6476230>
> .
>



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