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From "Derrick Burns (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-3219) K-Means clusterer should support Bregman distance functions
Date Wed, 17 Sep 2014 00:17:34 GMT

    [ https://issues.apache.org/jira/browse/SPARK-3219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14136564#comment-14136564
] 

Derrick Burns commented on SPARK-3219:
--------------------------------------

I went ahead and created a pull request.  I would appreciate your comments.

https://github.com/apache/spark/pull/2419




> K-Means clusterer should support Bregman distance functions
> -----------------------------------------------------------
>
>                 Key: SPARK-3219
>                 URL: https://issues.apache.org/jira/browse/SPARK-3219
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Derrick Burns
>            Assignee: Derrick Burns
>
> The K-Means clusterer supports the Euclidean distance metric.  However, it is rather
straightforward to support Bregman (http://machinelearning.wustl.edu/mlpapers/paper_files/BanerjeeMDG05.pdf)
distance functions which would increase the utility of the clusterer tremendously.
> I have modified the clusterer to support pluggable distance functions.  However, I notice
that there are hundreds of outstanding pull requests.  If someone is willing to work with
me to sponsor the work through the process, I will create a pull request.  Otherwise, I will
just keep my own fork.



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