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.
翻译:在加工采矿领域,存在几种不同的微量集束办法,将痕量或过程情况分成相似组别,这种分解通常基于某些模式或痕量之间的相似性,或由发现每个群集的流程模型所驱动。然而,这些技术的主要缺点是,其解决办法通常很难由域专家加以评估或说明理由。在本文件中,我们介绍了两种有限的微量集用技术,它们能够以实例限制的形式利用专家知识。在使用两个真实的数据集进行的广泛实验性评价中,我们表明我们的新技术确实能够产生更合理的集群解决办法,而不会对其质量产生重大的负面影响。