Decision mining revisited - Discovering overlapping rules

Felix Mannhardt*, Massimiliano De Leoni, Hajo A. Reijers, Wil M P Van Der Aalst

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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 nondeterminism 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.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings
PublisherSpringer - Verlag
Number of pages16
ISBN (Print)9783319396958
Publication statusPublished - 2016
Event28th International Conference on Advanced Information Systems Engineering, CAiSE 2016 - Ljubljana, Slovenia
Duration: 13 Jun 201617 Jun 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference28th International Conference on Advanced Information Systems Engineering, CAiSE 2016


  • Decision mining
  • Overlapping rules
  • Process mining


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