Decision Mining Revisited - Discovering Overlapping Rules

  • Felix MannhardtEmail author
  • Massimiliano de Leoni
  • Hajo A. Reijers
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)


Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules, which only allow one out of multiple activities to be performed. These methods assume that decision making is fully deterministic, and all factors influencing decisions are recorded. In case the underlying decision rules are overlapping due to non-determinism or incomplete information, the rules returned by existing methods do not fit the recorded data well. This paper proposes a new technique to discover overlapping decision rules, which fit the recorded data better at the expense of precision, using decision tree learning techniques. An evaluation of the method on two real-life data sets confirms this trade off. Moreover, it shows that the method returns rules with better fitness and precision in under certain conditions.


Decision mining Process mining Overlapping rules 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Felix Mannhardt
    • 1
    • 2
    Email author
  • Massimiliano de Leoni
    • 1
  • Hajo A. Reijers
    • 1
    • 3
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Lexmark Enterprise SoftwareNaardenThe Netherlands
  3. 3.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

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