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https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15758577#comment-15758577
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Apache Spark commented on SPARK-18808:
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User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/16328
> ml.KMeansModel.transform is very inefficient
> --------------------------------------------
>
> Key: SPARK-18808
> URL: https://issues.apache.org/jira/browse/SPARK-18808
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.0.2
> Reporter: Michel Lemay
>
> The function ml.KMeansModel.transform will call the
> parentModel.predict(features) method on each row which in turns will
> normalize all clusterCenters from mllib.KMeansModel.clusterCentersWithNorm
> every time!
> This is a serious waste of resources! In my profiling,
> clusterCentersWithNorm represent 99% of the sampling!
> This should have been implemented with a broadcast variable as it is done in
> other functions like computeCost.
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