I think I agree with this for clusters that are intended for human consumption, but I am sure that I disagree with this if you are looking to use the clusters internally for machine learning purposes.
The basic idea for the latter is that the distances to a bunch of clusters can be used as a description of a point. This description in terms of distances to cluster centroids can make some machine learning tasks vastly easier. On Mon, Jan 4, 2010 at 11:44 AM, Dawid Weiss <[email protected]> wrote: > What's worse -- neither method is "better". We at Carrot2 have a > strong feeling that clusters should be described properly in order to > be useful, but one may argue that in many, many applications of > clustering, the labels are _not_ important and just individual > features of clusters (like keywords or even documents themselves) are > enough. > -- Ted Dunning, CTO DeepDyve
