I was using a dynamic time warping (DTW) distance with KMedoids, which made more sense than using euclidean distance since the profiles indeed had warps along the time axis. DTW implementation was taken from MLPY since it's not in Scikit-Learn either.
Is it required that an algorithm, which is implemented in Scikit-Learn, scales well wrt n_samples? Would be nice to try Voronoi iteration Sebastian mentioned: https://en.wikipedia.org/wiki/Lloyd%27s_algorithm Cheers, Timo On Thu, Jul 30, 2015 at 10:12 PM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > 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. > >>> > >>> > ------------------------------------------------------------------------------ > >>> _______________________________________________ > >>> Scikit-learn-general mailing list > >>> Scikit-learn-general@lists.sourceforge.net > >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >> > >> > ------------------------------------------------------------------------------ > >> _______________________________________________ > >> Scikit-learn-general mailing list > >> Scikit-learn-general@lists.sourceforge.net > >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > > Scikit-learn-general mailing list > > Scikit-learn-general@lists.sourceforge.net > > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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