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.

在流程挖掘领域中，存在几种不同的跟踪聚类方法，用于将跟踪或流程实例划分为相似的组。通常，此分区基于迹线之间的某些模式或相似性，或者由发现每个群集的过程模型来驱动。但是，这些技术的主要缺点是它们的解决方案通常很难由领域专家进行评估或证明其合理性。在本文中，我们介绍了两种约束跟踪聚类技术，它们能够以实例级约束的形式利用专家知识。在使用两个真实数据集的广泛实验评估中，我们证明了我们的新技术确实能够产生更合理的聚类解决方案，而不会对其质量产生实质性的负面影响。