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From "Pallavi Palleti (JIRA)" <>
Subject [jira] Commented: (MAHOUT-153) Implement kmeans++ for initial cluster selection in kmeans
Date Mon, 18 Jan 2010 09:26:54 GMT


Pallavi Palleti commented on MAHOUT-153:

Hi all,

I am ready with my patch. However, I was trying to see if there is any possible optimizations
that can be made. I will share the patch and seek further optimization suggestions from the
group. Should I open another jira issue as David might be working on and submit a patch to
this jira issue? Kindly suggest.

> Implement kmeans++ for initial cluster selection in kmeans
> ----------------------------------------------------------
>                 Key: MAHOUT-153
>                 URL:
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Clustering
>    Affects Versions: 0.2
>         Environment: OS Independent
>            Reporter: Panagiotis Papadimitriou
>             Fix For: 0.3
>   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:
> 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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