[
https://issues.apache.org/jira/browse/FLINK-2131?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15556922#comment-15556922
]
ASF GitHub Bot commented on FLINK-2131:
---------------------------------------
Github user sachingoel0101 commented on the issue:
https://github.com/apache/flink/pull/757
I'll update based on your comments in a few days. ^^
On Oct 8, 2016 06:21, "Stavros Kontopoulos" <[email protected]>
wrote:
> @sachingoel0101 <https://github.com/sachingoel0101> @tillrohrmann
> <https://github.com/tillrohrmann> any plans for this PR?
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub
> <https://github.com/apache/flink/pull/757#issuecomment-252364165>, or mute
> the thread
>
<https://github.com/notifications/unsubscribe-auth/AIdpFR7VFxxqFaf_74WUxwKsOrGhOi1Eks5qxrfugaJpZM4E0H5N>
> .
>
> Add Initialization schemes for K-means clustering
> -------------------------------------------------
>
> Key: FLINK-2131
> URL: https://issues.apache.org/jira/browse/FLINK-2131
> Project: Flink
> Issue Type: Task
> Components: Machine Learning Library
> Reporter: Sachin Goel
> Assignee: Sachin Goel
>
> The Lloyd's [KMeans] algorithm takes initial centroids as its input. However,
> in case the user doesn't provide the initial centers, they may ask for a
> particular initialization scheme to be followed. The most commonly used are
> these:
> 1. Random initialization: Self-explanatory
> 2. kmeans++ initialization: http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
> 3. kmeans|| : http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
> For very large data sets, or for large values of k, the kmeans|| method is
> preferred as it provides the same approximation guarantees as kmeans++ and
> requires lesser number of passes over the input data.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
