But we need to set the iterations before calculating intracluster distance. I presume, only after we call the RepresenterPointsDriver.run() we would be able to get the intra cluster distance. I am not sure how is it going to help.
On Thu, Nov 1, 2012 at 9:41 PM, paritosh ranjan <[email protected]>wrote: > If the intra cluster distance is small ( which means the vectors are > tightly clustered ), then you might not need a lot of iterations to > represent it. > Similarly, if there are very few vectors per cluster, and the intra cluster > distance is also small, then even a single iteration would be fine. Thats > how I see it. > > On Thu, Nov 1, 2012 at 9:12 PM, Rahul Mishra <[email protected] > >wrote: > > > Thanks for the prompt reply Paritosh. > > Could you please explain it a bit further? How does it depend? > > > > Thanks & Regards, > > Rahul > > > > > > On Thu, Nov 1, 2012 at 8:44 PM, paritosh ranjan > > <[email protected]>wrote: > > > > > Each iteration will add a single point to the evolving list of > > > representative points for each cluster. > > > So, I think it depends on the number of vectors per cluster and also > the > > > intra cluster distance. > > > > > > On Thu, Nov 1, 2012 at 8:13 PM, Rahul Mishra <[email protected] > > > >wrote: > > > > > > > Hello Friends, > > > > > > > > Whats the heuristic for providing what number of iterations for > > > > RepresentativePointsDriver? > > > > > > > > I have run kmeans and fuzzy-kmeans algorithm on a dataset of size > > 500MB. > > > > Now, how do I obtain cluster quality? > > > > > > > > Does the following look Okay? : > > > > RepresentativePointsDriver.run(conf, new Path(clustersIn), new > > > > Path(clusteredPointsIn), new Path(outputDir), new > > > > EuclideanDistanceMeasure(), numIterations, runSequential); > > > > double interDis = clusterEval.interClusterDensity(); > > > > double intraDis = clusterEval.intraClusterDensity(); > > > > System.out.println("cluster evaluator: The inter distance: > "+interDis); > > > > System.out.println("cluster evaluator: The intra distance: > "+intraDis); > > > > > > > > > > > > > > > > -- > > > > Regards, > > > > Rahul K Mishra, > > > > https://sites.google.com/site/reachrahulkmishra/ > > > > > > > > > > > > > > > -- > > Regards, > > Rahul K Mishra, > > https://sites.google.com/site/reachrahulkmishra/ > > > -- Regards, Rahul K Mishra, https://sites.google.com/site/reachrahulkmishra/
