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

["Rohini Uppuluri (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Rohini Uppuluri updated MAHOUT-153:
-----------------------------------

    Attachment: MAHOUT-153_RandomFarthest.patch

Hi, 

This is the patch for creating random farthest cluster initialization. This does not have junit test cases yet.

Thanks,
-Rohini

> 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_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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