Well, distance is dependent upon the distance measure you want to use. A post-processing step could easily calculate this. The ClusterEvaluator may have some methods that could be useful. It calculates a set of representative points for each cluster and calculates interCluster and intraCluster densities from that.
-----Original Message----- From: Grant Ingersoll [mailto:[email protected]] Sent: Wednesday, July 13, 2011 1:28 PM To: [email protected] Subject: Re: Emitting distance from centroid for K-Means Good to know. Next question, what's the preferred way, then, to get out either the distance or what Ted said? -Grant On Jul 13, 2011, at 4:25 PM, Ted Dunning wrote: > I take back what I said. > > Jeff is correct. > > On Wed, Jul 13, 2011 at 1:23 PM, Jeff Eastman <[email protected]> wrote: > >> The weight is the probability the vector is a member of the cluster. For >> FuzzyK and Dirichlet it is fractional, for KMeans it is 1 as the algorithm >> is maximum likelihood and each point is only assigned to a single cluster. >> >> -----Original Message----- >> From: Grant Ingersoll [mailto:[email protected]] >> Sent: Wednesday, July 13, 2011 1:11 PM >> To: [email protected] >> Subject: Emitting distance from centroid for K-Means >> >> Does it make sense to output the distance to the cluster as the weight in >> the KMeansClusterer.outputPointWithClusterInfo method instead of 1? What's >> the purpose of the 1 as the weight? >> >> -Grant >> >> >> -------------------------- Grant Ingersoll
