Process mining helps organizations improve their business processes in today’s data-rich environment. However, these processes can change over time due to factors like policy shifts or process trends, impacting model performance. This study examines process behavior in event logs and uses machine learning to detect concept drift.
The trace clustering and change mining techniques have been implemented on two processes, namely loan payment and temporary identity creation, to detect drifts. We use the bag-of-activities and edit distance methods, along with K-Mode and agglomerative hierarchical clustering techniques.
This study makes two important findings: trace clustering is a popular choice for detecting drifts, and the bag-of-activities method using K-Mode clustering and hamming distance proved highly effective at spotting drifts in various event logs. It also identifies different types of drifts occurring simultaneously in the processes.
The drifts discovered in different processes provide a real-world example of concept drift in the domains of loans and university administrations. This contributes to improving operational efficiency and overall organizational performance based on these detected drifts and assists in enhancing the process design.
This study is the first to employ a hybrid method of trace clustering and change mining to detect process changes. It is also the first to simultaneously detect sudden and recurring drift in the field of trace clustering in process mining. Furthermore, it stands out for investigating and comparing the performance of multiple clustering methods, in contrast to prior research that used a single technique. Additionally, it is pioneering in applying machine learning methods to detect drift in the domain of loan processes.
