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


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:
>             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:
> []
> I also have a small prototype of this algorithm written in R (if anyone is interested
in it).

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