Expert-driven Trace Clustering with Instance-level Constraints

TL;DR

This paper introduces two new trace clustering methods in process mining that incorporate expert knowledge through instance-level constraints, resulting in more justifiable clusters without sacrificing quality.

Contribution

The paper presents novel constrained trace clustering techniques that leverage expert knowledge, improving interpretability and justification of clustering solutions in process mining.

Findings

Clusters are more justifiable with expert constraints.

Clustering quality is maintained despite added constraints.

Techniques outperform unconstrained methods in real datasets.

Abstract

Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, however, is that their solutions are usually hard to evaluate or justify by domain experts. In this paper, we present two constrained trace clustering techniques that are capable to leverage expert knowledge in the form of instance-level constraints. In an extensive experimental evaluation using two real-life datasets, we show that our novel techniques are indeed capable of producing clustering solutions that are more justifiable without a substantial negative impact on their quality.