[jira] [Resolved] (SPARK-8540) KMeans-based outlier detection

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-8540?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-8540.
------------------------------
    Resolution: Won't Fix

I agree with Joseph's comment that this may be a step beyond the de facto scope of MLlib at the moment. K-means is in scope but specializing for "outlier detection" feels like an external lib.

> KMeans-based outlier detection
> ------------------------------
>
>                 Key: SPARK-8540
>                 URL: https://issues.apache.org/jira/browse/SPARK-8540
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Joseph K. Bradley
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Proposal for K-Means-based outlier detection:
> * Cluster data using K-Means
> * Provide prediction/filtering functionality which returns outliers/anomalies
> ** This can take some threshold parameter which specifies either (a) how far off a point needs to be to be considered an outlier or (b) how many outliers should be returned.
> Note this will require a bit of API design, which should probably be posted and discussed on this JIRA before implementation.



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