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Posted to issues@spark.apache.org by "Ashutosh Trivedi (JIRA)" <ji...@apache.org> on 2014/10/22 06:20:33 UTC
[jira] [Updated] (SPARK-4038) Outlier Detection Algorithm for MLlib
[ https://issues.apache.org/jira/browse/SPARK-4038?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ashutosh Trivedi updated SPARK-4038:
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Description:
The aim of this JIRA is to discuss about which parallel outlier detection algorithms can be included in MLlib.
The one which I am familiar with is Attribute Value Frequency (AVF). It scales linearly with the number of data points and attributes, and relies on a single data scan. It is not distance based and well suited for categorical data. In original paper a parallel version is also given, which is not complected to implement. I am working on the implementation and soon submit the initial code for review.
Here is the Link for the paper
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4410382
As pointed out by Xiangrui in discussion
http://apache-spark-developers-list.1001551.n3.nabble.com/MLlib-Contributing-Algorithm-for-Outlier-Detection-td8880.html
There are other algorithms also. Lets discuss about which will be more general and easily paralleled.
was:
The aim of this JIRA is to discuss about which parallel outlier detection algorithms can be included in MLlib.
The one which I am familiar with is Attribute Value Frequency (AVF). It scales linearly with the number of data points and attributes, and relies on a single data scan. It is not distance based and well suited for categorical data. In original paper a Parallel version is also given, which is not complected to implement.
Here is the Link for the paper
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4410382
> Outlier Detection Algorithm for MLlib
> -------------------------------------
>
> Key: SPARK-4038
> URL: https://issues.apache.org/jira/browse/SPARK-4038
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Ashutosh Trivedi
>
> The aim of this JIRA is to discuss about which parallel outlier detection algorithms can be included in MLlib.
> The one which I am familiar with is Attribute Value Frequency (AVF). It scales linearly with the number of data points and attributes, and relies on a single data scan. It is not distance based and well suited for categorical data. In original paper a parallel version is also given, which is not complected to implement. I am working on the implementation and soon submit the initial code for review.
> Here is the Link for the paper
> http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4410382
> As pointed out by Xiangrui in discussion
> http://apache-spark-developers-list.1001551.n3.nabble.com/MLlib-Contributing-Algorithm-for-Outlier-Detection-td8880.html
> There are other algorithms also. Lets discuss about which will be more general and easily paralleled.
>
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