Compound trace clustering to generate accurate and simple sub-process models

Abstract

Business process model discovery targets the construction of conceptual models from event data that has been recorded during the execution of a business process. While a plethora of discovery techniques have been proposed in the literature, most existing techniques fail to cope with complex control-flow patterns as they are observed in event logs of highly flexible processes. In this paper, we follow the idea of splitting-up an event log into sub-logs, before applying process model discovery. This yields a set of sub-process models, one per sub-log, each describing a major variant of the business process. Unlike existing techniques, our clustering approach is guided by the result of model discovery: It first optimises the average complexity of the resulting models, before improving the accuracy of each model in isolation. Our experimental evaluation highlights that our approach yields more accurate sub-process models (that are of comparatively low complexity) than state-of-the-art trace clustering techniques.