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https://issues.apache.org/jira/browse/SPARK-5405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14334027#comment-14334027
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Xiangrui Meng commented on SPARK-5405:
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Dimension reduction should be separated from the k-means implementation. We can
add distance-preserving methods as feature transformers. [~derrickburns] Could
you update the JIRA title?
> Spark clusterer should support high dimensional data
> ----------------------------------------------------
>
> Key: SPARK-5405
> URL: https://issues.apache.org/jira/browse/SPARK-5405
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Derrick Burns
> Labels: clustering
> Original Estimate: 504h
> Remaining Estimate: 504h
>
> The MLLIB clusterer works well for low (<200) dimensional data. However,
> performance is linear with the number of dimensions. So, for practical
> purposes, it is not very useful for high dimensional data.
> Depending on the data type, one can embed the high dimensional data into
> lower dimensional spaces in a distance-preserving way. The Spark clusterer
> should support such embedding.
> An example implementation that supports high dimensional data is here:
> https://github.com/derrickburns/generalized-kmeans-clustering
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