A Generic Framework for Trace Clustering in Process Mining

The goal of process discovery is to visualize event log data as a process model. In reality, however, these models are often highly complex. Process trace clustering is a well-studied and powerful technique to address this. It groups an event log into more cohesive sub logs, such that the discovered process models become less complex and easier to understand. Over the past 15 years, researchers proposed various approaches for trace clustering in process discovery. The developed approaches vary greatly with regard to algorithmic capacities, data characteristics, computational complexity, and integration of additional information. In this paper, we provide a state-of-the-art analysis of trace clustering by a) performing a systematic literature review, and b) proposing a generic framework for trace clustering. Eventually, our goal is to provide an overview of current trace clustering research and a basis for developing new methods and approaches to trace clustering.