Abstract—Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. Process mining techniques have recently received notable attention in the literature for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Real-life processes tend to be less structured and more flexible. An approach to overcome this is to cluster process instances such that each of the resulting clusters corresponds to coherent sets of process instances that can each be adequately represented by a process model. On the other hand the conformance checker methods check if model and the log conform to each other or not. This paper proposed an approach to use Appropriateness Conformance Checker methods to split the event log into homogeneous subsets and for each subset a process model is created. To illustrate this we present a real-life case study from reality mining dataset provided by MIT (Massachusetts Institute of Technology) Media Laboratory. The whole approach has been implemented in ProM the process mining framework.
Index Terms—Process mining, workflow mining, reality mining, process clustering, conformance checker.
O. M. Hassan is with the Department of Mathematics, Facility of Science Al-Azhar University Cairo, Egypt (e-mail: ohassan@azhar.edu.eg).
M. S. Farag is with the Department of Mathematics, Facility of Science Al-Azhar University Cairo, Egypt (e-mail: farag.sayed@azhar.edu.eg).
Cite: O. M. Hassan and M. S. Farag, "Constructing Workflow Clusters Based On Appropriateness Conformance Checker," International Journal of Modeling and Optimization vol. 2, no. 3, pp. 255-259, 2012.
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