Abstract
Process discovery is the most important task in the process mining. Because of the complexity of event logs (i.e. activities of several different processes are written into the same log), the discovered process models may be diffuse and unintelligible. That is why the input event logs should be clustered into simpler event sub-logs. This work provides a trace clustering solution based on the idea of using the distance graph model for trace representation. Experimental results proved the effect of the proposed solution on two measures of Fitness and Precision, especially the effect on the Precision measure.
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This work was supported in part by VNU Grant QG-15- 22.
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Ha, QT., Bui, HN., Nguyen, TT. (2016). A Trace Clustering Solution Based on Using the Distance Graph Model. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_29
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DOI: https://doi.org/10.1007/978-3-319-45243-2_29
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