Shlomy, I will try to paraphrase what I understood: a. You are interested in the centroid for its own sake, rather than the cluster. b. An ideal centroid will have only zeros or ones as its coordinates, and thus create a summary of the cluster. c. The current Tanimoto implementation gives you a different centroid, with many small coordinate values.
I do not know enough of the math for K-means, but you could implement your own distance function, that will somehow penalize non-integral coordinates/distances. I am not sure if such a function will generate a converging K-means algorithm, but it might be worth a shot. If you are looking for summaries of vector sets, you can try matrix factorizations (PCA,SVD,NNMF), and see which dominant components come out. HTH, Yuval On Thu, Jun 21, 2012 at 2:43 PM, Sean Owen <[email protected]> wrote: > Erm, I think I am thinking of canopy clustering. > > For k-means, I suppose you could say the choice of the k means isn't > quite right if k-1 of them are nowhere near most points. I don't know > how they were chosen. But again maybe not the real issue at heart > here. > > On Thu, Jun 21, 2012 at 12:36 PM, Shlomy Boshy <[email protected]> > wrote: > > Interesting - to which thresholds do you refer? >
