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Posted to dev@mahout.apache.org by "Ted Dunning (JIRA)" <ji...@apache.org> on 2010/01/04 20:47:54 UTC
[jira] Commented: (MAHOUT-153) Implement kmeans++ for initial
cluster selection in kmeans
[ https://issues.apache.org/jira/browse/MAHOUT-153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12796323#action_12796323 ]
Ted Dunning commented on MAHOUT-153:
------------------------------------
{quote}
On Mon, Jan 4, 2010 at 4:03 AM, Palleti, Pallavi <pa...@corp.aol.com> 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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