On 18 January 2013 08:52, Gael Varoquaux <notifications@github.com> wrote:

> In doc/modules/clustering.rst:
>
> > -:class:`AffinityPropagation` clusters data by diffusion in the similarity
> > -matrix. This algorithm automatically sets its numbers of cluster. It
> > -will have difficulties scaling to thousands of samples.
> > +:class:`AffinityPropagation` creates clusters by sending messages between
> > +pairs of samples until convergence. A dataset is then described using a small
> > +number of exemplars, which are identified as those most representative of other
> > +samples. The messages sent between pairs represent the suitability for one
> > +sample to be the exemplar of the other, which is updated in response to the
> > +values from other pairs. This updating happens iteratively until convergence,
> > +at which point the final exemplars are chosen, and hence the final clustering
> > +is given.
> > +
> > +Affinity Propagation has a number of advantages over other algorithms. In many
> > +experiments it is shown to produce a lower error than other algorithms,
> > +specifically k-means, but it also works without any parameters, choosing the
> > +number of clusters based on the data provided.
>
> On Thu, Jan 17, 2013 at 01:47:47PM -0800, Robert Layton wrote: > +, but it
> also works without any parameters, choosing the > +number of clusters based
> on the data provided.
> By the way, that's just propaganda: the number of clusters is just
> replaced by another parameter, that is very hard to set. I know it is an
> argument that keeps being put forward for affinity propagation, but we
> should really give a balanced point of view, and point out that the problem
> is simply displaced.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/1589/files#r2688187>.
>
>

Are you talking about the lambda value (the damping factor)? If so I'd
argue it's more like the same value for most gradient descent -- more
controls the speed of learning than the outcome (although that isn't
necessarily true with local minima).

I have to say, I'm not as familiar with this algorithm as the others I've
done so far, so let me know if I've misunderstood something.


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