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https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12840554#action_12840554
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Robin Anil commented on MAHOUT-153:
-----------------------------------
Hi Rohini and Pallavi
Thanks for updating the patches. Could you guys give a short
description on what the input and output is? Seems this is all based on text
and mahout is using document vectors as the input and a configurable distance
measure. I just want to make sure this gets in quickly. So an input output
description will be of great help
> 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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