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From "Chiwan Park (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1731) Add kMeans clustering algorithm to machine learning library
Date Thu, 07 May 2015 13:44:01 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14532636#comment-14532636
] 

Chiwan Park commented on FLINK-1731:
------------------------------------

Hi, [~peedeeX21]. There are implementations of distance measure between two vectors including
Euclidean Distance in FLINK-1933. I have sent a [PR|https://github.com/apache/flink/pull/629]
and it will be merged soon. You can use it for calculating distances between each pair of
data.

> 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: Alexander Alexandrov
>              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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