Could you try running FuzzyKMeans and verify everything is alright i.e its infact clustering properly
Robin On Wed, Feb 17, 2010 at 5:07 PM, Pallavi Palleti < pallavi.pall...@corp.aol.com> wrote: > Hi Jeff, > > The semantics of center and centroid in fuzzy kmeans are same as k-means. > Center will be used to compute distance during iterations where as centroid > will be computed at the end of one iteration to get the new centroid. > > Thanks > Pallavi > > > > Jeff Eastman wrote: > >> Looks to me like the unit tests are the only calls to recomputeCenter, >> which is where the center is set. The clusterer seems to be calling >> computeCentroid, which sets the centroid, instead. I'm not sure why it needs >> both instance variables, as the pointProbSum and weightedPointTotal >> variables take the place of the single pointTotal in ClusterBase. I think >> perhaps center and centroid need to be merged? >> >> In k-means and canopy, the center is the (read-only) current centroid >> which is used for the distance calculations during an iteration, and it is >> recomputed by computeCentroid (using pointTotal and numPoints) at the end of >> the iteration. >> >> Jeff >> >> >> Robin Anil wrote: >> >>> I have been trying to convert FuzzyKMeans SoftCluster(which should be >>> ideally be named FuzzyKmeansCluster) to use the ClusterBase. >>> >>> I am getting* the same center* for all the clusters. To aid the >>> conversion >>> all i did was remove the center vector from the SoftCluster class and >>> reuse >>> the same from the ClusterBase. These are essentially making no change in >>> the >>> tests which passes correctly. >>> >>> So I am questioning whether the implementation keeps the average center >>> at >>> all ? Anyone who has used FuzzyKMeans experiencing this? >>> >>> >>> Robin >>> >>> >>> >> >>
