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.
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Notes
- 1.
\(N\) is trace-deterministic if the mapping \(u\in Runs (N) \mapsto \lambda (u) \in \varSigma ^*\) is injective.
- 2.
- 3.
Pseudo-Boolean constraints are generalizations of Boolean constraints. They allow one to specify constant bounds on the number of variables which can/must be assigned to true among a set \(V\) of variables.
- 4.
Due to AMSTC being the most general trace clustering, our experiments focus on this method.
- 5.
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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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Boltenhagen, M., Chatain, T., Carmona, J. (2019). Generalized Alignment-Based Trace Clustering of Process Behavior. In: Donatelli, S., Haar, S. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2019. Lecture Notes in Computer Science(), vol 11522. Springer, Cham. https://doi.org/10.1007/978-3-030-21571-2_14
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