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https://issues.apache.org/jira/browse/SPARK-5226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14290523#comment-14290523
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Muhammad-Ali A'rabi commented on SPARK-5226:
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That's right. For very huge data, it won't be a good implementation.
It is O(log n), actually. In preprocessing phase, we created a sorted map or
something, and with a radius, we can retrieve all points with less distance in
O(log n).
If we use the first implementation, for each region query we have to calculate
lots of distances, and some of them are surely calculated before.
We can have both ways implemented, and user may use any of them depending on
their need.
We can also use vector with norm and use the upper bound. But I don't trust
this method and have to test it.
> Add DBSCAN Clustering Algorithm to MLlib
> ----------------------------------------
>
> Key: SPARK-5226
> URL: https://issues.apache.org/jira/browse/SPARK-5226
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Muhammad-Ali A'rabi
> Priority: Minor
> Labels: DBSCAN
>
> MLlib is all k-means now, and I think we should add some new clustering
> algorithms to it. First candidate is DBSCAN as I think.
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