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https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12796323#action_12796323
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Ted Dunning commented on MAHOUT-153:
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{quote}
On Mon, Jan 4, 2010 at 4:03 AM, Palleti, Pallavi <[email protected]>
wrote:
Initially, I used canopy clustering seeds as initial seeds but the results
weren't good and the number of clusters depends on the distance thresholds we
give as input. Later, I have considered randomly selecting some points from the
input dataset and consider them as initial seeds. Again, the results were not
good. Now, I have chosen initial seeds from input set in such a way that the
points are far from each other and I have observed better clustering using
Fuzzy Kmeans. I have not implemented a map-reducable version for this seed
selection. I will soon implement a map-reducable version and submit a patch.
{quote}
I encouraged Pallavi on the mailing list to submit his patches here on this
issue. Hopefully he will be able to drive the process forward.
> 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
> 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: 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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