Decision Mining Revisited - Discovering Overlapping Rules

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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.
Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering
Subtitle of host publication28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings
EditorsS. Nurcan, P. Soffer, M. Bajec, J. Eder
Place of PublicationBerlin
Number of pages16
ISBN (Electronic)978-3-319-39696-5
ISBN (Print)978-3-319-39695-8
Publication statusPublished - 21 May 2016
Event28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) - Ljubljana, Slovenia
Duration: 13 Jun 201617 Jun 2016
Conference number: 28

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference28th International Conference on Advanced Information Systems Engineering (CAiSE 2016)
Abbreviated titleCAiSE '16
Other"Information Systems for Connecting People"
Internet address


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