Joseph K. Bradley created SPARK-8540:
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Summary: 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
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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