So what are you guys doing to get from an unpredictable number of canopies to a 'k' value for k-means and an initial assignment of each item to one cluster?
On Thu, Jun 11, 2009 at 12:49 PM, Adil Aijaz<[email protected]> wrote: > Jeff, > > Thanks for the quick turnaround on this issue. Just tested it and the canopy > creation and kmeans both work now on syntheticcontroldata. I get 7 canopies > and 7 clusters. Collection logic in close() is not pretty but can't think of > a workaround myself. > > adil > > Jeff Eastman wrote: >> >> r783617 removed the CanopyCombiner and refactored its semantics back into >> the reducer. Updated unit tests pass and Synthetic Control with Canopy >> produces 6 clusters. Kmeans also runs produces 6 clusters too. I really >> don't like doing stuff in close() but see no practical alternative. Ideas >> are still welcomed. >> >> Jeff >> >> >> Jeff Eastman wrote: >>> >>> Adil Aijaz wrote: >>>> >>>> 2. There is a bug in >>>> examples/src/main/java/org/apache/mahout/clustering/syntheticcontrol/kmeans/Job.java >>>> that called runJob from main function with my provided arguments >>>> transposed. >>>> So, my convergenceDelta was interpreted as t1, t1 as t2, and t2 as >>>> convergenceDelta. I will commit a patch as soon as I get approval for >>>> opensource commits from my employer, however, I thought I'd put it out >>>> there >>>> in case someone else is going through the same issue. >>>> >>> r783585 fixed the parameter ordering bug. Still working on the Combiner >>> problem. >>> >>> Thanks Adil, >>> Jeff >>> >>> >> > >
