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Detecting anomalies in work practice data by combining multiple domains of information

  • US 9,264,442 B2
  • Filed: 04/26/2013
  • Issued: 02/16/2016
  • Est. Priority Date: 04/26/2013
  • Status: Active Grant
First Claim
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1. A computer-executable method for multi-domain clustering, comprising:

  • receiving, by a computing device, from one or more computing devices over a network, domain data for at least two domains associated with users, wherein a domain is a source type describing observable activities of a plurality of users, and wherein a respective user is associated with a respective user role in an organization;

    determining a hyperparameter value for a respective prior distribution of a probability distributing associated with a domain;

    estimating a probability distribution for generating a multi-domain probability model;

    generating a multi-domain probability model that includes variables for two or more domains, based on the hyperparameter value and the estimated probability distribution;

    analyzing the domain data with the generated multi-domain probability model to assign a first user to a plurality of cluster indices associated with the first user'"'"'s corresponding user role; and

    determining that an activity being performed by a second user is anomalous, based on a comparison between one or more of the second user'"'"'s cluster indices to one or more of the first user'"'"'s plurality of cluster indices.

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