Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

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

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the
executions of the process differ widely from each other due to contextual factors, human …

Trace clustering has been extensively used to discover aspects of the data from event logs.
Process Mining techniques guide the identification of sub-logs by grouping traces with …

This study looked into the problems that occur when a clustering or cluster ensemble model
with unsupervised or semi-supervised learning is used in a real-world setting. However …

Process mining provides various algorithms to analyze process executions based on event
data. Process discovery, the most prominent category of process mining techniques, aims to …

The current business environment has no room for inefficiency as it can cause companies to
lose out to their competitors, to lose customer trust, and to experience cost overruns …

A strategy for fair supervised learning is proposed. It involves formulating an optimization
problem to minimize loss subject to a prescribed bound on a measure of unfairness (eg …

Process mining encompasses a series of tasks aimed at discovering knowledge about
business processes from event logs underlying information systems deployed in …

Business processes have undergone a significant transformation with the advent of the
process-oriented view in organizations. The increasing complexity of business processes …