A Descriptive Clustering Approach to the Analysis of Quantitative Business-Process Deviances

Increasing attention has been paid to the problem of explaining and analyzing "deviant cases" generated by a business process, i.e. instances of the process that diverged from prescribed/expected behavior (e.g. frauds, faults, SLA violations). In many real settings, such cases are labelled with a numerical deviance measure, and the analyst wants to obtain a fine grain unsupervised classification of them, which will help her recognize and explain different deviance scenarios. Unfortunately, current approaches rely on preliminary labelling all the cases, stored in some an execution log, as either deviant or non-deviant, and then inducing a rule-based classifier for discriminating among the two classes. By contrast, we here propose a method that discovers accurate and readable deviance- aware clusters (of cases) defined in terms of descriptive rules over both properties and behavioral aspects of the cases. Each cluster is also equipped with summary information that allows to derive effective distribution charts and a high-level process map, both emphasizing the distinctive features of the cluster. Tests on a real-life log confirmed the ability of the approach to find easily-interpretable clustering models capturing relevant deviance scenarios.