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https://issues.apache.org/jira/browse/SPARK-14174?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16512214#comment-16512214
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zhengruifeng commented on SPARK-14174:
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[~mlnick] [~mengxr] [~josephkb] Mini-Batch KMeans is much faster than KMeans,
do you have any plan to involve it in MLLIb? Thanks
> Implement the Mini-Batch KMeans
> -------------------------------
>
> Key: SPARK-14174
> URL: https://issues.apache.org/jira/browse/SPARK-14174
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: zhengruifeng
> Priority: Major
> Attachments: MBKM.xlsx
>
>
> The MiniBatchKMeans is a variant of the KMeans algorithm which uses
> mini-batches to reduce the computation time, while still attempting to
> optimise the same objective function. Mini-batches are subsets of the input
> data, randomly sampled in each training iteration. These mini-batches
> drastically reduce the amount of computation required to converge to a local
> solution. In contrast to other algorithms that reduce the convergence time of
> k-means, mini-batch k-means produces results that are generally only slightly
> worse than the standard algorithm.
> Comparison of the K-Means and MiniBatchKMeans on sklearn :
> http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#example-cluster-plot-mini-batch-kmeans-py
> Since MiniBatch-KMeans with fraction=1.0 is not equal to KMeans, so I make it
> a new estimator
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