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https://issues.apache.org/jira/browse/MAHOUT-384?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12859865#action_12859865
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Ted Dunning commented on MAHOUT-384:
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Outlier detection is (normally) unsupervised exploratory learning.
Occasionally it is used to generate a feature for supervised learning, much as
clustering algorithms can be used.
As such, I would group it as a clustering into "normal" and "outlier" clusters.
It won't evaluate the same way, but it definitely has the same workflow.
> Implement of AVF algorithm
> --------------------------
>
> Key: MAHOUT-384
> URL: https://issues.apache.org/jira/browse/MAHOUT-384
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Reporter: tony cui
> Attachments: mahout-384.patch
>
>
> This program realize a outlier detection algorithm called avf, which is kind
> of
> Fast Parallel Outlier Detection for Categorical Datasets using Mapreduce and
> introduced by this paper :
> http://thepublicgrid.org/papers/koufakou_wcci_08.pdf
> Following is an example how to run this program under haodoop:
> $hadoop jar programName.jar avfDriver inputData interTempData outputData
> The output data contains ordered avfValue in the first column, followed by
> original input data.
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