LDA Ensembles for Interactive Exploration and Categorization of Behaviors

We define behavior as a set of actions performed by some agent during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple agents, more specifically, identifying typical behaviors as well as spotting behavior anomalies. We propose an approach leveraging topic modeling techniques -- LDA (Latent Dirichlet Allocation) Ensembles -- for representing categories of typical behaviors by topics obtained through applying topic modeling to a behavior collection. When such methods are applied to text documents, the goodness of the extracted topics is usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, may not be applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Our case studies in the operation behaviors in the security management system and visiting behaviors in an amusement park and the expert evaluation of the first case study demonstrate the effectiveness of our approach.