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https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14606437#comment-14606437
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ASF GitHub Bot commented on FLINK-1731:
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Github user peedeeX21 commented on the pull request:
https://github.com/apache/flink/pull/700#issuecomment-116850459
I am having some trouble to fit our predictor into the new API.
The problem is, that with `PredictOperation` the type of the model has to
be defined. A `DataSet` of this type is the output of the `getModel`. For the
`predict` method the input is just an object of this type.
In our case our model is a `DataSet` of `LabeledVectors` (the centroids).
This means I can not implement a `PredictOperation` due to that restriction.
For me the API feels a bit inconsistent in that case
For now I implemented only an `PredictDataSetOperation`.
> 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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