[SPARK-8540] KMeans-based outlier detection

• Type: Sub-task
• Status: Resolved
• Priority: Major
• Resolution: Won't Fix
• Component/s: ML
• Assignee: Unassigned
• Reporter: Joseph K. Bradley
• Created: 22/Jun/15 22:12
• Updated: 25/Jan/16 09:56
• Resolved: 25/Jan/16 09:56

Description

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.