On Sun, Mar 25, 2012 at 11:22:32PM +0200, Andreas wrote: > >> I'm not sure if "flat geometry" is a good way to describe the case that > >> KMeans works in. I would have said "convex clusters". Not sure in how far > >> that applies to hierarchical clustering, though.
> > Euclidean distance. > Can you please elaborate? 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. > Just know I have 4 plots of clustering algorithms on my desktop, > one of which I want to publish. Problem is: the clustering does not > agree with the classes, while the kmeans results do. > Now what? -_- That's a well known problem: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.2501 > Basically Shi/Malik multipy by the inverse diagonal from the right while > Jordan/Ng multiply by the square root form left and right - or something ;) But if you do it right, the two approaches solve the same problem, AFAIK. Gaël ------------------------------------------------------------------------------ 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
