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Posted to dev@mahout.apache.org by "Ted Dunning (JIRA)" <ji...@apache.org> on 2010/09/21 19:56:34 UTC
[jira] Commented: (MAHOUT-153) Implement kmeans++ for initial
cluster selection in kmeans
[ https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12913121#action_12913121 ]
Ted Dunning commented on MAHOUT-153:
------------------------------------
This has a patch and tests. can Pallavi or Rohini stand up and say what the situation is here? Is it ready?
> Implement kmeans++ for initial cluster selection in kmeans
> ----------------------------------------------------------
>
> Key: MAHOUT-153
> URL: https://issues.apache.org/jira/browse/MAHOUT-153
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Affects Versions: 0.2
> Environment: OS Independent
> Reporter: Panagiotis Papadimitriou
> Assignee: Ted Dunning
> Fix For: 0.4
>
> Attachments: Mahout-153.patch, Mahout-153.patch, Mahout-153.patch, MAHOUT-153_RandomFarthest.patch
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> The current implementation of k-means includes the following algorithms for initial cluster selection (seed selection): 1) random selection of k points, 2) use of canopy clusters.
> I plan to implement k-means++. The details of the algorithm are available here: http://www.stanford.edu/~darthur/kMeansPlusPlus.pdf.
> Design Outline: I will create an abstract class SeedGenerator and a subclass KMeansPlusPlusSeedGenerator. The existing class RandomSeedGenerator will become a subclass of SeedGenerator.
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