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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14504671#comment-14504671
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Raghav Chalapathy commented on FLINK-1745:
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Hi Chiwan, Till
I totally agree with Chiwan and Till's idea to make it a generic trait Distance
measure; to support various distance metrics such as Euclidean, Manhattan etc
Going by the literature shared in
Approach 1 : https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
We must start of with the basic :
Exact approaches HBNLJ, H-BRJ, ( Compare the cost )
Approximate approach : zkNN
As a part of implementation homework I was going through some of the
implementations and stumbled accross this one
https://github.com/codeAshu/SparkAlgorithms/tree/master/mllib/src/main/scala/org/sparkalgos/mllib/join
Issue has be considered here :
https://issues.apache.org/jira/browse/SPARK-2335
Approach 2: The paper they have referred to is :
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5447837&tag=1&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5447837%26tag%3D1
My Question is : Have we done a comparision of Approach 1 and Approach 2 ?
Should we perform a comparision study going forward ?
Raghav
> Add 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: Chiwan Park
> 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.
> 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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