You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Derrick Burns (JIRA)" <ji...@apache.org> on 2014/12/09 05:17:13 UTC
[jira] [Commented] (SPARK-3219) K-Means clusterer should support
Bregman distance functions
[ https://issues.apache.org/jira/browse/SPARK-3219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14238958#comment-14238958 ]
Derrick Burns commented on SPARK-3219:
--------------------------------------
Any progress on this pull request?
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org