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https://issues.apache.org/jira/browse/SPARK-3219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14336182#comment-14336182
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Derrick Burns commented on SPARK-3219:
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I have implemented Bregman divergences (the most general class of distance
functions for which the K-Means algorithm can be proven to converge) in
https://github.com/derrickburns/generalized-kmeans-clustering
> 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
> Labels: clustering
>
> 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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