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Transactions on Petri Nets and Other Models of Concurrency XII pp 19–42Cite as

Similarity-Based Approaches for Determining the Number of Trace Clusters in Process Discovery

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Part of the book series: Lecture Notes in Computer Science ((TOPNOC,volume 10470))

Abstract

Given the complexity of real-life event logs, several trace clustering techniques have been proposed to partition an event log into subsets with a lower degree of variation. In general, these techniques assume that the number of clusters is known in advance. However, this will rarely be the case in practice. Therefore, this paper presents approaches to determine the appropriate number of clusters in a trace clustering context. In order to fulfil the objective of identifying the most appropriate number of trace clusters, two approaches built on similarity are proposed: a stability- and a separation-based method. The stability-based method iteratively calculates the similarity between clustered versions of perturbed and unperturbed event logs. Alternatively, an approach based on between-cluster dissimilarity, or separation, is proposed. Regarding practical validation, both approaches are tested on multiple real-life datasets to investigate the complementarity of the different components. Our results suggest that both methods are successful in identifying an appropriate number of trace clusters.

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Notes

  1. 1.

    This approach is implemented as an experimental ProM-plugin which can be found on http://www.processmining.be/clusterstability/.

  2. 2.

    For more info on the XES-standard, we refer to http://www.xes-standard.org/.

  3. 3.

    The first two methods are implemented in the ProM-framework for process mining in the ActiTrac-plugin. The latter five methods are implemented in the GuideTree-Miner-plugin.

  4. 4.

    The visual representations of the MCRM- and MOA-event logs are available on http://www.processmining.be/clusterstability/ToPNoCResults.

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Authors and Affiliations

  1. Research Center for Management Informatics, Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Pieter De Koninck & Jochen De Weerdt

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  1. Pieter De Koninck
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  2. Jochen De Weerdt
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Correspondence to Pieter De Koninck .

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Editors and Affiliations

  1. Newcastle University, Newcastle upon Tyne, United Kingdom

    Maciej Koutny

  2. LIACS, Leiden University, Leiden, The Netherlands

    Jetty Kleijn

  3. Polish Academy of Sciences, Institute of Computer Science, Warsaw, Poland

    Wojciech Penczek

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De Koninck, P., De Weerdt, J. (2017). Similarity-Based Approaches for Determining the Number of Trace Clusters in Process Discovery. In: Koutny, M., Kleijn, J., Penczek, W. (eds) Transactions on Petri Nets and Other Models of Concurrency XII. Lecture Notes in Computer Science(), vol 10470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55862-1_2

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  • DOI: https://doi.org/10.1007/978-3-662-55862-1_2

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