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Process discovery aims at automatically creating process models on the basisof event data captured during the execution of business processes. Processdiscovery algorithms tend to use all of the event data to discover a processmodel. This attitude sometimes leads to discover imprecise and/or complexprocess models that may conceal important information of processes. To addressthis problem, several techniques, from data filtering to model repair, havebeen elaborated in the literature. In this paper, we introduce a newincremental prototype selection algorithm based on clustering of processinstances. The method aims to iteratively compute a unique process model with adifferent set of selected prototypes, i.e., representative of whole event dataand stops when conformance metrics decrease. The proposed method has beenimplemented in both the ProM and the RapidProM platforms. We applied theproposed method on several real event data with state-of-the-art, processdiscovery algorithms. Results show that using the proposed method leads toimprove the general quality of discovered process models.

Prototype Selection Based on Clustering and Conformance Metrics for Model Discovery