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https://issues.apache.org/jira/browse/SPARK-3219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14119654#comment-14119654
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Yu Ishikawa commented on SPARK-3219:
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I am willing to work with you.
Could you please let me know your forked branch ?

Because I had thought that KMeans supports any other distance function, 
I contributed some distance function to Breeze.

> 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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