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From "Hae Joon Lee (JIRA)" <j...@apache.org>
Subject [jira] [Issue Comment Deleted] (FLINK-1731) Add kMeans clustering algorithm to machine learning library
Date Tue, 12 May 2015 16:55:00 GMT

     [ https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Hae Joon Lee updated FLINK-1731:
--------------------------------
    Comment: was deleted

(was: To load input `Points` in fit function for `BreezeVector` we should use input:  DataSet[LebelVector]?
I implemented input dataset like trainingData = seq[DenseVector] (DenseVector(-0.489811986685,
0.496883004904, -0.483860999346) ... )

In the case of K-means, datatype of `Centroids` can be LebelVector because it has centroid
number, but datatype of `Points` does not have to be LebelVector in that it only has points
as coordinates.)

> 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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