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https://issues.apache.org/jira/browse/SPARK-8540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14600076#comment-14600076
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Joseph K. Bradley commented on SPARK-8540:
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That's correct: For (b), the user would specify wanting the K most anomalous
data points (or perhaps some fraction).
(a) seems more reasonable statistically, but (b) would let users collect the
results without fear of blowing up the master node.
> 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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