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A unified approach for measuring precision and generalization based on anti-alignments
Dongen, Boudewijn van; Carmona Vargas, Josep; Chatain, Thomas
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
The holy grail in process mining is an algorithm that, given an event log, produces fitting, precise, properly generalizing and simple process models. While there is consensus on the existence of solid metrics for fitness and simplicity, current metrics for precision and generalization have important flaws, which hamper their applicability in a general setting. In this paper, a novel approach to measure precision and generalization is presented, which relies on the notion of antialignments. An anti-alignment describes highly deviating model traces with respect to observed behavior. We propose metrics for precision and generalization that resemble the leave-one-out cross-validation techniques, where individual traces of the log are removed and the computed anti-alignment assess the model’s capability to describe precisely or generalize the observed behavior. The metrics have been implemented in ProM and tested on several examples.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació
Data mining
Enterprise resource management
Management science
Statistical methods
In-process
Leave-one-out cross validations
Measure precision
Measuring precision
Process model
Unified approach
Mineria de dades
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
Springer
         

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