Abstract Machine learning (ML) provides algorithms to create computer programs based on
data without explicitly programming them. In business process management (BPM), ML …

The goal of process discovery is to visualize event log data as a process model. In reality,
however, these models are often highly complex. Process trace clustering is a well-studied …

This paper focuses on the cluster description problem where, given a dataset and its
partition into clusters, the task is to explain the clusters. We introduce a new approach to …

Process mining techniques are used to extract knowledge about the efficiency and
compliance of an organization's business processes through process models. Real-life …

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster
Analysis. Our goal is to find an explanation for each cluster, such that clusters are …

Clustering is a popular unsupervised learning tool often used to discover groups within a
larger population such as customer segments, or patient subtypes. However, despite its use …

Clustering is an unsupervised machine learning technique whose goal is to cluster
unlabeled data. But traditional clustering methods only output a set of results and do not …

In recent years, much of the research on clustering algorithms has primarily focused on
enhancing their accuracy and efficiency, frequently at the expense of interpretability …

Abstract Recently, Massive Open Online Courses (MOOCs) have experienced rapid
development. However, one of the major issues of online education is the high dropout rates …

This paper presents a systematic literature review (SLR) on the explainability and
interpretability of machine learning (ML) models within the context of predictive process …