[jira] [Commented] (MAHOUT-153) Implement kmeans++ for initial cluster selection in kmeans

["Ted Dunning (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13037119#comment-13037119 ] 

Ted Dunning commented on MAHOUT-153:
------------------------------------

Going twice!

Pallavi?  Rohini?

Anybody else?  Is this patch worth committing?  k-means++ should be a substantial improvement for our k-means.  But without somebody pushing, it can't happen.

> 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
>         Attachments: MAHOUT-153_RandomFarthest.patch, Mahout-153.patch, Mahout-153.patch, Mahout-153.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.

--
This message is automatically generated by JIRA.
For more information on JIRA, see: http://www.atlassian.com/software/jira