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From "zhengruifeng (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-14174) Implement the Mini-Batch KMeans
Date Thu, 22 Jun 2017 08:21:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-14174?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

zhengruifeng updated SPARK-14174:
---------------------------------
    Attachment: MBKM.xlsx

> 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
>         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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