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ANOMALY DETECTION FOR CONTEXT-DEPENDENT DATA

  • US 20160328654A1
  • Filed: 05/04/2015
  • Published: 11/10/2016
  • Est. Priority Date: 05/04/2015
  • Status: Abandoned Application
First Claim
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1. A method directed for detecting anomalies in monitored data having plurality of data-segments partitioned to context related initial-subspaces, said method comprising:

  • training an association-map between said initial-subspaces and feature-clusters of said plurality of data-segments, said training is responsive to a fit-criterion;

    concatenating said initial-subspaces into cluster-subspaces, responsive to being associated to similar said feature-clusters according to said association-map, to obtain a generalized-association-map;

    pinpointing at least one anomaly of at least one new data-segment of said data, responsive to deviation-criterion for deviation of said new data-segment from its associated one of said feature-clusters, according to said generalized-association-map; and

    triggering an automatic-act responsive to a trigger-criterion for said at least one anomaly.

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