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https://issues.apache.org/jira/browse/SPARK-2966?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14122266#comment-14122266
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RJ Nowling commented on SPARK-2966:
-----------------------------------
No worries.
Based on my reading of the Spark contribution guidelines (
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark ), I
think that the Spark community would prefer to have one good implementation of
an algorithm instead of multiple similar algorithms.
Since the community has stated a clear preference for divisive hierarchical
clustering, I think that is a better aim. You seem very motivated and have
made some good contributions -- would you like to take the lead on the
hierarchical clustering? I can review your code to help you improve it.
That said, I suggest you look at the comment I added to SPARK-2429 and see what
you think of that approach. If you like the example code and papers, why don't
you work on implementing it efficiently in Spark?
> Add an approximation algorithm for hierarchical clustering to MLlib
> -------------------------------------------------------------------
>
> Key: SPARK-2966
> URL: https://issues.apache.org/jira/browse/SPARK-2966
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Yu Ishikawa
> Priority: Minor
>
> A hierarchical clustering algorithm is a useful unsupervised learning method.
> Koga. et al. proposed highly scalable hierarchical clustering altgorithm in
> (1).
> I would like to implement this method.
> I suggest adding an approximate hierarchical clustering algorithm to MLlib.
> I'd like this to be assigned to me.
> h3. Reference
> # Fast agglomerative hierarchical clustering algorithm using
> Locality-Sensitive Hashing
> http://dl.acm.org/citation.cfm?id=1266811
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