I'm a noob when it comes to data on a sphere, but is there any issue
with preprocessing the data to project it on a place, run kmeans in
the plane and the reproject it back on the sphere?

There's no projection that conserves the distance wrt to any pair of
points on the sphere (although there are some that conserves the
distance wrt 1 or 2 specific points on the sphere)

BUT the gnomonic project conserves the shortest path between any pair
of points on half a sphere. Maybe that's enough for you?
http://en.wikipedia.org/wiki/Gnomonic_projection

--
Cp


On Thu, Jan 24, 2013 at 9:55 AM, Vince Fernando <y...@vincefernando.co.uk> 
wrote:
> 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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