[jira] [Resolved] (SPARK-6137) G-Means clustering algorithm implementation

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6137?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-6137.
------------------------------
    Resolution: Won't Fix

> 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
>              Labels: clustering
>
> 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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