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https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14552243#comment-14552243
 ] 

ASF GitHub Bot commented on FLINK-1731:
---------------------------------------

GitHub user peedeeX21 opened a pull request:

    https://github.com/apache/flink/pull/700

    [FLINK-1731] [ml] Implementation of Feature K-Means and Test Suite

    Within the IMPRO-3 warm-up task the implementation of K-Means and 
corresponding test suite was done.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/peedeeX21/flink feature_kmeans

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/700.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #700
    
----
commit 02fe6b2c7ebc6bf4b55e832681286994b03c4d40
Author: Florian Goessler <m...@floriangoessler.de>
Date:   2015-05-20T09:12:20Z

    [FLINK-1731] [ml] unit test for KMeans

commit 71aa47bd06ad2e051749ea1b9df923b8eb5bf6e4
Author: Peter Schrott <peter.schrot...@gmail.com>
Date:   2015-05-20T11:08:36Z

    [FLINK-1731] [ml] Implementation of K-Means

----


> Add kMeans clustering algorithm to machine learning library
> -----------------------------------------------------------
>
>                 Key: FLINK-1731
>                 URL: https://issues.apache.org/jira/browse/FLINK-1731
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Peter Schrott
>              Labels: ML
>
> The Flink repository already contains a kMeans implementation but it is not 
> yet ported to the machine learning library. I assume that only the used data 
> types have to be adapted and then it can be more or less directly moved to 
> flink-ml.
> The kMeans++ [1] and the kMeans|| [2] algorithm constitute a better 
> implementation because the improve the initial seeding phase to achieve near 
> optimal clustering. It might be worthwhile to implement kMeans||.
> Resources:
> [1] http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
> [2] http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf



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