Multi-objective trace clustering: Finding more balanced solutions

Abstract

In recent years, a multitude of techniques has been proposed for the task of clustering traces. In general, these techniques either focus on optimizing their solution based on a certain type of similarity between the traces, such as the number of insertions and deletions needed to transform one trace into another; by mapping the traces onto a vector space model, based on certain patterns in each trace; or on the quality of a process model discovered from each cluster. Currently, the main technique of the latter category, ActiTraC, constructs its clusters based on a single objective: fitness. However, a typical view in process discovery is that one needs to balance fitness, generalization, precision and simplicity. Therefore, a multi-objective approach to trace clustering is deemed more appropriate. In this paper, a thorough overview of current trace clustering techniques and potential approaches for multi-objective trace clustering is given. Furthermore, a multi-objective trace clustering technique is proposed. Our solution is shown to provide unique results on a number of real-life event logs, validating its existence.