Interactive Process Clustering with t-SNE

Steffen Schuhmann, Jana-Rebecca Rehse, Sebastian Baumann, Peter Fettke

In: BPM 2020. BPM Demo Track (BPMTracks) September 14-18 Sevilla Spain Springer 9/2020.


Process trace clustering is a well-studied and powerful tech- nique to support the discovery of high-quality process models. It splits an event log into more cohesive sublogs, such that the discovered process models are easier to read and to understand. However, existing clustering approaches typically optimize measures like fitness or precision instead of focusing on the model understandability and utility, as assessed by a pro- cess analyst. In addition, they offer no opportunity to influence or adapt the clustering result according to the analyst’s use case or preferences. In this paper, we propose an interactive tool to trace clustering based on the t-SNE algorithm. Traces are represented in a two-dimensional graph, where they can be selected interactively for process discovery. We also offer the user some guidance with a predefined selection of possible clus- ters. Using this system, a process analyst is able to find a representative set of process models for each event log without any knowledge in pro- gramming and a basic understanding of the used discovery techniques.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence