[GitHub] spark issue #11974: [SPARK-14174][ML][WIP] Accelerate KMeans via Mini-Batch ...

[MLnick <gi...@git.apache.org>]

Github user MLnick commented on the issue:

    https://github.com/apache/spark/pull/11974
  
    @sethah the idea is to get to a "good enough" solution in a fraction of the time. The [paper](http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) shows it achieving "near optimal" cost in far fewer iterations. Scikit-learn has [MiniBatchKMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html) and shows a comparison with KMeans that indicates the resulting clustering is typically [very similar](http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html).
    
    Though @zhengruifeng the above comparison should probably also compare the "fully converged" full data solution (cost and # iters) with the minibatch solution (cost and # iters). It is not clear from what you posted what that result is?
    
    Actually I also forgot that we will definitely need to update the examples and also add a section in the guide explaining the use of `miniBatchFraction` and explaining the tradeoffs and providing references...


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