Article,
Process Mining Based on Clustering: A Quest for Precision
Business Process Management Workshops, (2008)
Abstract
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. Mostof these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult todetermine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniquesproduce 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 acoherent set of cases that can be adequately represented by a process model. The approach allows for different clusteringand process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalizationand a process discovery algorithm that is much more robust than the algorithms described in literature 1. The whole approachhas been implemented in ProM.
