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2016 | OriginalPaper | Buchkapitel

Decision Mining Revisited - Discovering Overlapping Rules

verfasst von : Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers, Wil M. P. van der Aalst

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Predictive Business Process Monitoring Framework with Hyperparameter Optimization

Nächstes Kapitel An Adaptability-Driven Model and Tool for Analysis of Service Profitability

The data used for the evaluation is available under http://purl.tue.nl/844997340832257. For confidentiality reasons we cannot share the sepsis event log.

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Titel: Decision Mining Revisited - Discovering Overlapping Rules
verfasst von: Felix Mannhardt
Massimiliano de Leoni
Hajo A. Reijers
Wil M. P. van der Aalst
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-319-39695-8

Electronic ISBN: 978-3-319-39696-5

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-39696-5_23

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