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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6137) G-Means clustering algorithm implementation
Date Tue, 03 Mar 2015 20:03:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6137?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14345635#comment-14345635
] 

Joseph K. Bradley commented on SPARK-6137:
------------------------------------------

Yeah, I'm not aware of a theoretical motivation for the splitting used in Streaming K-Means.

GMeans sounds valuable to me, and the results in the paper look nice.  I do wonder if the
accuracy difference with X-means in the paper's results is more an issue with tuning parameters;
it would be interesting to see results testing whether one algorithm's hyper-parameter (alpha
for GMeans vs. weighting the model complexity penality in X-Means) was easier to tune than
the other, though that might require a whole lot of testing.

> G-Means clustering algorithm implementation
> -------------------------------------------
>
>                 Key: SPARK-6137
>                 URL: https://issues.apache.org/jira/browse/SPARK-6137
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Denis Dus
>            Priority: Minor
>
> Will it be useful to implement G-Means clustering algorithm based on K-Means?
> G-means is a powerful extension of k-means, which uses test of cluster data normality
to decide if it necessary to split current cluster into new two. It's relative complexity
(compared to k-Means) is O(K), where K is maximum number of clusters. 
> The original paper is by Greg Hamerly and Charles Elkan from University of California:
> [http://papers.nips.cc/paper/2526-learning-the-k-in-k-means.pdf]
> I also have a small prototype of this algorithm written in R (if anyone is interested
in it).



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