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

["Rakesh Chalasani (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-8540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15088746#comment-15088746 ] 

Rakesh Chalasani commented on SPARK-8540:
-----------------------------------------

I see that this hasn't moved forward, so trying to revive it. I will pick this up.

After taking a fleeting glance at the KMeans API, we have two options:

1. Add this to KMeans/KMeansModel itself (which I don't like after what [~josephkb] said above)
 
(or)

2. We need KMeansOutlier and KMeansOutlierModel as separate classes; KMeansOutlier can extend KMeans itself with additional parameters for supporting the above mentioned (a) and (b). KMeansOutlierModel might have to duplicate some parts of KMeansModel

For (a) setThreshold/getThreshold param need to be added and can be implemented using simple 'where'; (b) setNumOutliers/getNumOutliers param need to be added and requires orderBy followed by take (or something better?). (a) takes precedence over (b).

Please let me know your thoughts.


> 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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