Scalable Mixed-Paradigm Trace Clustering using Super-Instances

Abstract

In process mining, one is often confronted with datasets that contain high degrees of control-flow variation. This causes the results of subsequent process mining steps to be less accurate or harder to understand. Trace clustering, or dividing the log into more homogeneous groups, can help limit this issue. Trace clustering techniques are commonly subdivided into two types: distance-based techniques and model-driven approaches. In this paper, a novel trace clustering technique is presented, that combines aspects of both paradigms. The core idea is to first learn so-called super-instances using a simple distance-driven technique and subsequently apply a model-driven technique to cluster the super-instances. Our technique not only shows qualitative improvements of the obtained clusterings, but also significantly improves the scalability, as shown in an experimental evaluation using real-life event logs.

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