On 03/25/2012 11:47 PM, Gael Varoquaux wrote: > On Sun, Mar 25, 2012 at 11:38:50PM +0200, Andreas wrote: > >>> Unlike something like spectral clustering, it is the euclidean distance >>> to the centers that is minimized. Thus K-Means will seek clusters that >>> are regular in the flat euclidean space. >>> > >> Ok, that's right. Though I would argue that the distance measure >> is not the only factor here. MeanShift also works with a euclidean >> distance by using a Gaussian weighted density. >> Here the difference is more in the objective then in the underlying >> distance. >> > Granted. Do you have suggestions for a better formulation? It would be > helpful. > > As far as I can see, your groups are "KMeans + Ward" and "rest". I don't know how ward works but looking at the lena example, the clusters don't seem to be convex. I'll try to understand the algorithm before I'll suggest anything - maybe I should have done that before criticising your wording ;)
> >>> But if you do it right, the two approaches solve the same problem, AFAIK. >>> > >> They lead to different generalized eigenvalue problems: >> http://arxiv.org/pdf/0711.0189 >> > Yes (that's exactly the reference that I had in mind), but they should > minimize the same energy, right? > > Skimming the tutorial, I am not sure what the answer to that question is. I think they are both relaxations of the normalized cuts problem, but lead to different solutions in gerneral. The tutorial suggests using Shi/Malik but since both are in use in the literature, I thought it would be nice to have them both. ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
