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Generalized Alignment-Based Trace Clustering of Process Behavior

  • Mathilde BoltenhagenEmail author
  • Thomas ChatainEmail author
  • Josep CarmonaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11522)

Abstract

Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets.

Notes

Acknowledgments

This work has been supported by Farman institute at ENS Paris-Saclay and by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LSV, CNRS, ENS Paris-Saclay, InriaUniversité Paris-SaclayCachanFrance
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain

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