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https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Pallavi Palleti updated MAHOUT-153:
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Attachment: Mahout-153.patch
Here is the patch for selecting initial clusters for a clustering algorithm.
Idea is taken from paper "Farthest Point Heuristic Based Initialization Methods
for K-Modes Clustering"(http://arxiv.org/pdf/cs/0610043).
The attached patch follow below steps:
The farthest-point heuristic starts with an arbitrary point s1. Pick a point s2
that is as far from
s1 as possible. Pick si to maximize the distance to the nearest of all
centroids picked so far. That is,
maximize the min {dist (si, s1), dist (si, s2), ...}. After all k
representatives are chosen we can define
the partition of D: cluster Cj consists of all points closer to sj than to any
other representative
> 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
>
> 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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