[
https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14608003#comment-14608003
]
ASF GitHub Bot commented on FLINK-1731:
---------------------------------------
Github user peedeeX21 commented on the pull request:
https://github.com/apache/flink/pull/700#issuecomment-117068361
I totally agree on you guys points. We have a little amount of centroids,
and the model is not supposed to be distributed in the end.
The question is now: Should the resulting `DataSet` of centroids just be
collected, or the the whole iteration be rewritten to work an a non distributed
collection?
Note: Unfortunately I am quite busy right now with other projects, so I
wont have time to do lots of changes right now. Either the people from my group
(who might actually have the same workload right now) or @sachingoel0101 can
work on that if its really urgent.
> 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
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
