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Using minimum description length for process mining

Published: 08 March 2009 Publication History

Abstract

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.

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cover image ACM Conferences
SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
March 2009
2347 pages
ISBN:9781605581668
DOI:10.1145/1529282
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 March 2009

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SAC09: The 2009 ACM Symposium on Applied Computing
March 8, 2009 - March 12, 2008
Hawaii, Honolulu

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2024)IMVis: Visual analytics for influence maximization algorithm evaluation in hypergraphsVisual Informatics10.1016/j.visinf.2024.04.0068:2(13-26)Online publication date: Jun-2024
  • (2024)I-PALIA: Discovering BPMN Processes with Duplicated Activities for Healthcare DomainsProcess Mining Workshops10.1007/978-3-031-56107-8_19(247-258)Online publication date: 13-Apr-2024
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