Abstract
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 anti-alignments. 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.
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Notes
- 1.
Throughout the paper, we will use P and G letters to denote precision and generalization metrics, respectively.
- 2.
Note that for the edit distance between the anti-alignment and the removed trace, the trace is first projected onto labeled elements, i.e. the \(\tau \) transition is removed first.
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Acknowledgments
This work has been partially supported by funds from the Spanish Ministry for Economy and Competitiveness (MINECO), the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).
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van Dongen, B.F., Carmona, J., Chatain, T. (2016). A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9850. Springer, Cham. https://doi.org/10.1007/978-3-319-45348-4_3
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DOI: https://doi.org/10.1007/978-3-319-45348-4_3
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