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From "Daniel Blazevski (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (FLINK-1934) Add approximative k-nearest-neighbours (kNN) algorithm to machine learning library
Date Mon, 02 Nov 2015 14:20:27 GMT

     [ https://issues.apache.org/jira/browse/FLINK-1934?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Daniel Blazevski updated FLINK-1934:
------------------------------------
    Description: 
kNN is still a widely used algorithm for classification and regression. However, due to the
computational costs of an exact implementation, it does not scale well to large amounts of
data. Therefore, it is worthwhile to also add an approximative kNN implementation as proposed
in [1,2].  Reference [3] is cited a few times in [1], and gives necessary background on the
z-value approach.

Resources:
[1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
[2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
[3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf

  was:
kNN is still a widely used algorithm for classification and regression. However, due to the
computational costs of an exact implementation, it does not scale well to large amounts of
data. Therefore, it is worthwhile to also add an approximative kNN implementation as proposed
in [1,2].

Resources:
[1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
[2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
[3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf


> Add approximative k-nearest-neighbours (kNN) algorithm to machine learning library
> ----------------------------------------------------------------------------------
>
>                 Key: FLINK-1934
>                 URL: https://issues.apache.org/jira/browse/FLINK-1934
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Raghav Chalapathy
>              Labels: ML
>
> kNN is still a widely used algorithm for classification and regression. However, due
to the computational costs of an exact implementation, it does not scale well to large amounts
of data. Therefore, it is worthwhile to also add an approximative kNN implementation as proposed
in [1,2].  Reference [3] is cited a few times in [1], and gives necessary background on the
z-value approach.
> Resources:
> [1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
> [2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
> [3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf



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