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From "Alexander Alexandrov (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (FLINK-1731) Add kMeans clustering algorithm to machine learning library
Date Thu, 14 May 2015 14:36:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14543734#comment-14543734
] 

Alexander Alexandrov edited comment on FLINK-1731 at 5/14/15 2:35 PM:
----------------------------------------------------------------------

I would go with a {{DataSet}} for the centroids as well. That said, we can reduce syntax at
the client side by providing either

- an overloaded {{setCentroids(Seq\[A\])}} setter, or
- an implicit converter of type {{Seq\[A\] => DataSet\[A\]}} (needs to be part of the Flink
Scala API, could be already there) which allows to pass a {{Seq\[A\]}} argument to a {{setCentroids(DataSet\[A\])}}
setter.


was (Author: aalexandrov):
I would go with a {{DataSet}} for the centroids as well. That said, we can reduce syntax at
the client side by providing either

- an implicit converter that {{Seq\[A\] => DataSet\[A\]}} (needs to be part of the Flink
Scala API, could be already there), or
- an overloaded {{setCentroids(Seq\[A\])}} setter.

> 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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