  Process discovery aims at automatically creating process models on the basis
of event data captured during the execution of business processes. Process
discovery algorithms tend to use all of the event data to discover a process
model. This attitude sometimes leads to discover imprecise and/or complex
process models that may conceal important information of processes. To address
this problem, several techniques, from data filtering to model repair, have
been elaborated in the literature. In this paper, we introduce a new
incremental prototype selection algorithm based on clustering of process
instances. The method aims to iteratively compute a unique process model with a
different set of selected prototypes, i.e., representative of whole event data
and stops when conformance metrics decrease. The proposed method has been
implemented in both the ProM and the RapidProM platforms. We applied the
proposed method on several real event data with state-of-the-art, process
discovery algorithms. Results show that using the proposed method leads to
improve the general quality of discovered process models.
