Yes, I may be far more expensive than k-means. I just used it with Euclidean 
distance -- was for a comparison. I think k-medoids can still be useful for 
smaller, maybe noisier datasets, or if you have some distance measure were 
calculating averages may not make sense.


> On Jul 30, 2015, at 2:48 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> 
> I think KMediods has come up before.
> One issues is that it doesn't really scale to large n_samples, right?
> 
> There is an implementation mentioned here:
> https://github.com/scikit-learn/scikit-learn/issues/3799
> 
> Do you use it because you have a custom distance matrix?
> 
> On 07/30/2015 02:27 PM, Sebastian Raschka wrote:
>> I was looking for K-Medoids too couple of weeks ago and ended up 
>> implementing it myself -- but more like quick & dirty. I would really 
>> welcome a nice and efficient implementation of available via scikit, for 
>> example, using voronoi iteration.
>> 
>> Best,
>> Sebastian
>> 
>>> On Jul 30, 2015, at 1:51 PM, Timo Erkkilä <timo.erkk...@gmail.com> wrote:
>>> 
>>> Hi all,
>>> 
>>> I checked and could find no mention of KMedoids in Scikit-Learn. Me and my 
>>> friend have implemented the algorithm in Python, and were wondering if it 
>>> could be brought into Scikit-Learn. Thoughts?
>>> 
>>> 
>>> Cheers,
>>> Timo
>>> 
>>> 
>>> PS: I am new to the mailing list, so please guide me in case I am doing 
>>> something wrong here.
>>> 
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