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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1745) Add exact k-nearest-neighbours algorithm to machine learning library
Date Sun, 04 Oct 2015 16:32:26 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14942703#comment-14942703
] 

ASF GitHub Bot commented on FLINK-1745:
---------------------------------------

Github user danielblazevski commented on the pull request:

    https://github.com/apache/flink/pull/1220#issuecomment-145364401
  
    Thanks @chiwanpark for the very useful comments.  I have made changes to the comments,
which can be found here:
    https://github.com/danielblazevski/flink/tree/FLINK-1745/flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn
    
    I also changed the testing of KNN + QuadTree, which can be found here:
    https://github.com/danielblazevski/flink/tree/FLINK-1745/flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/nn
    
    Since useQuadTree is now a parameter, I did not need KNNQuadTreeSuite anymore and I removed
it.
    
    I did not address comment 6 yet.  I need to have the training set before I can define
a non-user specified useQuadTree, so any main if(useQuadTree) should come within ` val crossed
= trainingSet.cross(inputSplit).mapPartition {`
    
    About your last "P.S" comment,  Creating the quadtree after the cross operation is likely
more efficient -- each CPU/Node will form their own quadtree, which is what is suggested for
the R-tree here:
    https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
    
    This will result less communication overhead than creating a more global quadtree, if
that is what you were referring to.


> Add exact k-nearest-neighbours algorithm to machine learning library
> --------------------------------------------------------------------
>
>                 Key: FLINK-1745
>                 URL: https://issues.apache.org/jira/browse/FLINK-1745
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Daniel Blazevski
>              Labels: ML, Starter
>
> Even though the k-nearest-neighbours (kNN) [1,2] algorithm is quite trivial it is still
used as a mean to classify data and to do regression. This issue focuses on the implementation
of an exact kNN (H-BNLJ, H-BRJ) algorithm as proposed in [2].
> Could be a starter task.
> Resources:
> [1] [http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
> [2] [https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf]



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