Re: [jira] [Commented] (FLINK-1731) Add kMeans clustering algorithm to machine learning library

[Fabian Hueske <fh...@gmail.com>]

The owner of the repository can trigger as many builds on Travis as
required including rerunning failed builds.
The Apache repository is controlled by the ASF infra team, so we (the Flink
community) do not have the rights to retrigger builds.

To trigger an initial build on your repository, you can slightly change
your commit message and force push into your branch. That will give a new
commit hash and trigger a Travis build.

Cheers, Fabian

2015-06-05 12:26 GMT+02:00 Peter Schrott (JIRA) <ji...@apache.org>:

>
>     [
> https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14574273#comment-14574273
> ]
>
> Peter Schrott commented on FLINK-1731:
> --------------------------------------
>
> [~till.rohrmann] I am not entirely sure if we speak about the same thing.
> In our opinion the failure of Travis is not related to our changes.
> Or do you mean, that I should force Travis to run over my repository to
> see the problem still exists?
> If so, I just need to push something to my repository, right? But I don't
> have any changes to make.
> - Thanks, Peter
>
> > 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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