Generalized Alignment-Based Trace Clustering of Process Behavior

Mathilde Boltenhagen (1, 2) , Thomas Chatain (1, 2) , Josep Carmona (3)

1 LSV - Laboratoire Spécification et Vérification [Cachan]
2 MEXICO - Modeling and Exploitation of Interaction and Concurrency
3 UPC - Universitat Politècnica de Catalunya = Université polytechnique de Catalogne [Barcelona]

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 sub-net 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.

Domaines: Informatique [cs]

Mathilde Boltenhagen, Thomas Chatain, Josep Carmona. Generalized Alignment-Based Trace Clustering of Process Behavior. Petri Nets 2019 / ACSD 2019 - 40th International Conference on Application and Theory of Petri Nets and Concurrency, Jun 2019, Aachen, Germany. ⟨hal-02176771⟩