Applying Sequence Mining for Outlier Detection in Process Mining

  • Mohammadreza Fani SaniEmail author
  • Sebastiaan J. van Zelst
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11230)


One of the challenges in applying process mining algorithms on real event data, is the presence of outlier behavior. Such behaviour often leads to complex, incomprehensible, and, sometimes, even inaccurate process mining results. As a result, correct and/or important behaviour of the process may be concealed. In this paper, we exploit sequence mining techniques for the purpose of outlier detection in the process mining domain. Using the proposed approach, it is even possible to detect outliers in case of heavy parallelism and/or long-term dependencies between business process activities. Our method has been implemented in both the ProM- and the RapidProM framework. Using these implementations, we conducted a collection of experiments that show that we are able to detect and remove outlier behaviour in event data. Our evaluation clearly demonstrates that the proposed method accurately removes outlier behaviour and, indeed, improves process discovery results.


Process mining Sequence mining Event log filtering Event log preprocessing Sequential rule mining Outlier detection 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mohammadreza Fani Sani
    • 1
    Email author
  • Sebastiaan J. van Zelst
    • 2
  • Wil M. P. van der Aalst
    • 1
    • 2
  1. 1.Process and Data Science ChairRWTH Aachen UniversityAachenGermany
  2. 2.Fraunhofer FITSankt AugustinGermany

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