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Subgroup Discovery in Process Mining

  • Mohammadreza Fani Sani
  • Wil van der Aalst
  • Alfredo Bolt
  • Javier García-Algarra
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 288)

Abstract

Process mining enables multiple types of process analysis based on event data. In many scenarios, there are interesting subsets of cases that have deviations or that are delayed. Identifying such subsets and comparing process mining results is a key step in any process mining project.

We aim to find the statistically most interesting patterns of a subset of cases. These subsets can be created by process mining algorithms features (e.g., conformance checking diagnostics) and serve as input for other process mining techniques. We apply subgroup discovery in the process mining domain to generate actionable insights like patterns in deviating cases. Our approach is supported by the ProM framework. For evaluation, an experiment has been conducted using event data from a large Spanish telecommunications company. The results indicate that using subgroup discovery, we could extract interesting insights that could only be found by spitting the event data in the right manner.

Keywords

Process mining Subgroup discovery Pattern mining Performance management Quality of metrics 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammadreza Fani Sani
    • 1
  • Wil van der Aalst
    • 1
  • Alfredo Bolt
    • 1
  • Javier García-Algarra
    • 2
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.TelefonicaMadridSpain

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