You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Venkata Vineel (JIRA)" <ji...@apache.org> on 2015/07/06 09:04:04 UTC
[jira] [Commented] (SPARK-8540) KMeans-based outlier detection
[ https://issues.apache.org/jira/browse/SPARK-8540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14614630#comment-14614630 ]
Venkata Vineel commented on SPARK-8540:
---------------------------------------
[~josephkb] Can I please work on this(if you can mentor me with design etc.).
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org