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https://issues.apache.org/jira/browse/SPARK-29967?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16978942#comment-16978942
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zhengruifeng commented on SPARK-29967:
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[~srowen] I suggested move the impl, since if I recall correctly, there seems
some consensus on removing spark.mllib code in the future. I just find that in
[GMM's emgration from mllib to
ml|[https://github.com/apache/spark/pull/15413http://example.com],|http://example.com],]
[~josephkb] commented that 'As far as keeping the code around, I much prefer
either the current approach (separate code) or having spark.mllib call into
spark.ml. That will make it easier to deprecate and eventually remove
spark.mllib code in 3.0.'
Are we still aim to emigrate the impls? If above tree parts is too large, what
about spliting it into serveral sub-tasks?
> 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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