[jira] [Commented] (MADLIB-927) Initial implementation of k-NN

["ANISH SINGH (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15173113#comment-15173113 ] 

ANISH SINGH commented on MADLIB-927:
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Hello Rahul Sir,
I'm Anish, a sophomore CSE student. Last winter, I decided to develop a share price prediction program and started work on it. I decided to use Apache Spark ml libraries, but they did not contain a default implementation of k-NN algorithm and it has not been developed as of now. I extensively studied papers about the algorithm and find myself in a suitable position to work on this project for the entire Summer. I would like to request to be guided further about the issue so that I can study more about it and draw up my proposal. The completion of the project would facilitate my previous attempts at the share price prediction program.
Thank You.

> Initial implementation of k-NN
> ------------------------------
>
>                 Key: MADLIB-927
>                 URL: https://issues.apache.org/jira/browse/MADLIB-927
>             Project: Apache MADlib
>          Issue Type: New Feature
>            Reporter: Rahul Iyer
>              Labels: gsoc2016, starter
>
> k-Nearest Neighbors is a very simple algorithm that is based on finding nearest neighbors of data points in a metric feature space according to a specified distance function. It is considered one of the canonical algorithms of data science. It is a nonparametric method, which makes it applicable to a lot of real-world problems, where the data doesn’t satisfy particular distribution assumptions. Also, it can be implemented as a lazy algorithm, which means there is no training phase where information in the data is condensed into coefficients, but there is a costly testing phase where all data is used to make predictions.
> This JIRA involves implementing the naïve approach - i.e. compute the k nearest neighbors by going through all points.



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