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Posted to issues@flink.apache.org by "Daniel Blazevski (JIRA)" <ji...@apache.org> on 2015/11/02 15:13:27 UTC

[jira] [Updated] (FLINK-1934) Add approximative k-nearest-neighbours (kNN) algorithm to machine learning library

     [ 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].

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


> 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].
> 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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