hi Ariel,

what I would do, if the data are not too big, is reimplement my kmeans in
10 lines and after you update the centers, normalize them to put them back
on the sphere. I don't think you can say much about convergence but
it might work in practice.

HTH
Alex

On Thu, Jan 24, 2013 at 1:24 AM, Gael Varoquaux
<gael.varoqu...@normalesup.org> wrote:
> On Thu, Jan 24, 2013 at 12:34:31AM +0100, Andreas Mueller wrote:
>> Sorry, custom metrics for K means are not possible at the moment.
>
> Yes, there is a massive difference in amount of work and performance when
> you try to replace the Euclidean distance. Amongst other problems, the
> mean is no longer the sum divided by the number of points, but the
> Frechet mean, which requires solving an optimization problem.
>
> Ariel, quite often, I find that use of distances adapted to the unit
> sphere for clustering is over sold. If you have enough clusters, they do
> not extend much on the sphere, and thus the sphere is locally equivalent
> to a plane, and you can use standard Euclidean-based clustering
> algorithms. If not, you have to pay the price, and it will be expansive.
>
> G
>
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