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Online Induction of Probabilistic Real-Time Automata

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Abstract

The probabilistic real-time automaton (PRTA) is a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps: the creation of the prefix tree acceptor (PTA) and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded datasets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing them and then using a maximum frequent pattern based clustering. The approach is tested against a predefined synthetic automaton and real world datasets, for which the approach is scalable and stable. Moreover, we present a broad evaluation on a real world disease group dataset that shows the applicability of such a model to the analysis of medical processes.

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Authors and Affiliations

  1. Technische Universität München, Institut für Informatik/I12, Boltzmannstr. 3, 85748, Garching b. München, Germany

    Jana Schmidt & Stefan Kramer

  2. Johannes Gutenberg-Universität Mainz, Institute for Computer Science, Staudingerweg 9, 55128, Mainz, Germany

    Jana Schmidt & Stefan Kramer

Authors
  1. Jana Schmidt
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  2. Stefan Kramer
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Correspondence to Jana Schmidt.

Additional information

A preliminary version of the paper was published in the Proceedings of ICDM 2012.

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Schmidt, J., Kramer, S. Online Induction of Probabilistic Real-Time Automata. J. Comput. Sci. Technol. 29, 345–360 (2014). https://doi.org/10.1007/s11390-014-1435-8

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  • DOI: https://doi.org/10.1007/s11390-014-1435-8

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