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https://issues.apache.org/jira/browse/SPARK-2308?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14077952#comment-14077952
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Xiangrui Meng commented on SPARK-2308:
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Thanks for testing! Did you mean k-means|| initialization? We only use
k-means++ inside k-means||. Do you mind increasing the cluster sizes by 10x?
That is 4 centers of 10000, 1000, 100, and 10 data points. It might help
discover the smallest cluster during initialization.
> Add KMeans MiniBatch clustering algorithm to MLlib
> --------------------------------------------------
>
> Key: SPARK-2308
> URL: https://issues.apache.org/jira/browse/SPARK-2308
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: RJ Nowling
> Priority: Minor
> Attachments: many_small_centers.pdf, uneven_centers.pdf
>
>
> Mini-batch is a version of KMeans that uses a randomly-sampled subset of the
> data points in each iteration instead of the full set of data points,
> improving performance (and in some cases, accuracy). The mini-batch version
> is compatible with the KMeans|| initialization algorithm currently
> implemented in MLlib.
> I suggest adding KMeans Mini-batch as an alternative.
> I'd like this to be assigned to me.
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