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https://issues.apache.org/jira/browse/SPARK-29967?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16978952#comment-16978952
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Sean R. Owen commented on SPARK-29967:
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Yes, I think that is a fine idea, but nothing has really been migrated. For
consistency, might be fine to leave the core in .mllib and consider a mass
migration later. (If it's not going to make the change unwieldy.) I kind of
doubt it'll ever really be moved as there isn't a huge upside to breaking any
code using .mllib
> KMeans support instance weighting
> ---------------------------------
>
> Key: SPARK-29967
> URL: https://issues.apache.org/jira/browse/SPARK-29967
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Affects Versions: 3.0.0
> Reporter: zhengruifeng
> Priority: Major
>
> Since https://issues.apache.org/jira/browse/SPARK-9610, we start to support
> instance weighting in ML.
> However, Clustering and other impl in features still do not support instance
> weighting.
> I think we need to start support weighting in KMeans, like what scikit-learn
> does.
> It will contains three parts:
> 1, move the impl from .mllib to .ml
> 2, make .mllib.KMeans as a wrapper of .ml.KMeans
> 3, support instance weighting in the .ml.KMeans
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