Derrick Burns created SPARK-3219:
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Summary: K-Means clusterer should support Bregman distance metrics
Key: SPARK-3219
URL: https://issues.apache.org/jira/browse/SPARK-3219
Project: Spark
Issue Type: Improvement
Components: MLlib
Reporter: 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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