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https://issues.apache.org/jira/browse/SPARK-14516?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16069815#comment-16069815
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Marco Gaido edited comment on SPARK-14516 at 3/27/23 9:42 AM:
--------------------------------------------------------------

Hello everybody,

I have a proposal for a very efficient Silhouette implementation in a 
distributed environment. Here you can find the link with all the details of the 
solution. As soon as I will finish all the implementation and the tests I will 
post the PR for this: 
-[https://drive.google.com/file/d/0B0Hyo%5f%5fbG%5f3fdkNvSVNYX2E3ZU0/view|https://drive.google.com/file/d/0B0Hyo__bG_3fdkNvSVNYX2E3ZU0/view].-
 (please refer to https://arxiv.org/abs/2303.14102).

Please tell me if you have any comment, doubt on it.

Thanks.


was (Author: mgaido):
Hello everybody,

I have a proposal for a very efficient Silhouette implementation in a 
distributed environment. Here you can find the link with all the details of the 
solution. As soon as I will finish all the implementation and the tests I will 
post the PR for this: 
https://drive.google.com/file/d/0B0Hyo%5f%5fbG%5f3fdkNvSVNYX2E3ZU0/view.

Please tell me if you have any comment, doubt on it.

Thanks.


> Clustering evaluator
> --------------------
>
>                 Key: SPARK-14516
>                 URL: https://issues.apache.org/jira/browse/SPARK-14516
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Ruifeng Zheng
>            Assignee: Marco Gaido
>            Priority: Major
>             Fix For: 2.3.0
>
>
> MLlib does not have any general purposed clustering metrics with a ground 
> truth.
> In 
> [Scikit-Learn](http://scikit-learn.org/stable/modules/classes.html#clustering-metrics),
>  there are several kinds of metrics for this.
> It may be meaningful to add some clustering metrics into MLlib.
> This should be added as a {{ClusteringEvaluator}} class of extending 
> {{Evaluator}} in spark.ml.



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