> 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.
>> 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 > > I'm tempted but would like to avoid going down that road ;) >> 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. > > They lead to different generalized eigenvalue problems: http://arxiv.org/pdf/0711.0189 ------------------------------------------------------------------------------ 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
