[
https://issues.apache.org/jira/browse/SPARK-30661?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17488611#comment-17488611
]
zhengruifeng commented on SPARK-30661:
--------------------------------------
since the input datasets of kmeans are likely dense, so I tend to add a dense
impl as a alternative.
I think we can do it in this way:
step1: move existing impl to the .ml side. I think we should keep existing impl
to avoid possible regression cases;
step2: make the .mllib.kmeans call the .ml.kmeans internally. We also need to
support initialization with existing model in the .ml side, since .mllib.kmeans
supports this function;
step3: add the new dense impl. Make .ml.kmeans extends HasSolver, it will
supports three options: row-based (default), block-based, auto. If end user set
it auto, then the impl will check the sparsity and choose the underlying impl.
step4: sync the change to the python side.
cc [~srowen]
> KMeans blockify input vectors
> -----------------------------
>
> Key: SPARK-30661
> URL: https://issues.apache.org/jira/browse/SPARK-30661
> Project: Spark
> Issue Type: Sub-task
> Components: ML, PySpark
> Affects Versions: 3.0.0
> Reporter: zhengruifeng
> Assignee: zhengruifeng
> Priority: Minor
> Attachments: blockify_kmeans.png
>
>
--
This message was sent by Atlassian Jira
(v8.20.1#820001)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
