Are there any theoretical problems if one uses the great circle
(orthodromic) distance on a sphere in k-means or any other clustering
algorithm?
vince
On 24 January 2013 07:11, Mathieu Blondel <math...@mblondel.org> wrote:
> On Thu, Jan 24, 2013 at 9:24 AM, Gael Varoquaux
> <gael.varoqu...@normalesup.org> wrote:
>
> > 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.
>
> Indeed, if you replace the Euclidean distance, you also need to change
> the averaging.
> If you use the Manhattan distance, the averaging becomes the median.
>
> Mathieu
>
>
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