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Multi-objective Trace Clustering: Finding More Balanced Solutions

  • Pieter De Koninck
  • Jochen De Weerdt
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 281)

Abstract

In recent years, a multitude of techniques has been proposed for the task of clustering traces. In general, these techniques either focus on optimizing their solution based on a certain type of similarity between the traces, such as the number of insertions and deletions needed to transform one trace into another; by mapping the traces onto a vector space model, based on certain patterns in each trace; or on the quality of a process model discovered from each cluster. Currently, the main technique of the latter category, ActiTraC, constructs its clusters based on a single objective: fitness. However, a typical view in process discovery is that one needs to balance fitness, generalization, precision and simplicity. Therefore, a multi-objective approach to trace clustering is deemed more appropriate. In this paper, a thorough overview of current trace clustering techniques and potential approaches for multi-objective trace clustering is given. Furthermore, a multi-objective trace clustering technique is proposed. Our solution is shown to provide unique results on a number of real-life event logs, validating its existence.

Keywords

Trace clustering Process mining Process model quality Multi-objective learning 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Economics and Business, Research Center for Management InformaticsKU LeuvenLeuvenBelgium

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