A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)

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.

Keywords

Process Mining Edit Distance Single Trace Abstraction Mechanism Precision Metrics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.LSV, ENS Cachan, CNRS, INRIA, Universit Paris-SaclayCachanFrance

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