Using minimum description length for process mining

T.G.K. Calders, C.W. Günther, M. Pechenizkiy, A. Rozinat

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

39 Citations (Scopus)


In the field of process mining, the goal is to automatically extract process models from event logs. Recently, many algorithms have been proposed for this task. For comparing these models, different quality measures have been proposed. Most of these measures, however, have several disadvantages; they are model-dependent, assume that the model that generated the log is known, or need negative examples of event sequences. In this paper we propose a new measure, based on the minimal description length principle, to evaluate the quality of process models that does not have these disadvantages. To illustrate the properties of the new measure we conduct experiments and discuss the trade-off between model complexity and compression.
Original languageEnglish
Title of host publicationProceedings of the 2009 ACM Symposium on Applied Computing (SAC 2009, Honolulu HI, USA, March 8-12, 2009)
EditorsS.Y. Shin, S. Ossowski
Place of PublicationNew York NY
PublisherAssociation for Computing Machinery, Inc
ISBN (Print)978-1-60558-166-8
Publication statusPublished - 2009
Event24th ACM Symposium on Applied Computing (SAC 2009) - Hilton Hawaiian Village Beach Resort & Spa Waikiki Beach, Honolulu, United States
Duration: 9 Mar 200912 Mar 2009
Conference number: 24


Conference24th ACM Symposium on Applied Computing (SAC 2009)
Abbreviated titleSAC 2009
Country/TerritoryUnited States


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