[jira] [Commented] (SPARK-3219) K-Means clusterer should support Bregman distance functions

["Sai Nishanth Parepally (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14482297#comment-14482297 ] 

Sai Nishanth Parepally commented on SPARK-3219:
-----------------------------------------------

[~mengxr], is https://github.com/derrickburns/generalized-kmeans-clustering going to be merged into mllib as I would like to use "jaccard distance" as a distance metric for 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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