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https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14566564#comment-14566564
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Alexander Alexandrov commented on FLINK-1731:
---------------------------------------------
[~till.rohrmann] I coudn't find it eather. I think we were discussing to do the
K-Means|| as a separate issue.
Florian Gößler also reported the following issue when he tried to rebase
{{{
Error:(200, 75) ambiguous implicit values:
both value denseVectorConverter in object BreezeVectorConverter of type =>
org.apache.flink.ml.math.BreezeVectorConverter[org.apache.flink.ml.math.DenseVector]
and value sparseVectorConverter in object BreezeVectorConverter of type =>
org.apache.flink.ml.math.BreezeVectorConverter[org.apache.flink.ml.math.SparseVector]
match expected type org.apache.flink.ml.math.BreezeVectorConverter[T]
.reduce((p1, p2) => (p1._1, (p1._2.asBreeze +
p2._2.asBreeze).fromBreeze, p1._3 + p2._3))
}}}
Any idea what might be the cause?
> 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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