Process Mining Based on Clustering: A Quest for Precision

Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization an...

• Type: Conference
• Year: 2007
• Where: BPM
• Authors: Ana Karla Alves de Medeiros, Antonella Guzzo, Gianluigi Greco, Wil M. P. van der Aalst, A. J. M. M. Weijters, Boudewijn F. van Dongen, Domenico Saccà
• Keywords: BPM 2007, Business, Event Log, Process Mining Techniques, Process Model