Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time
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Most Process Mining techniques assume business processes remain steady through time, when in fact their underlying design could evolve over time. Discovery algorithms should be able to automatically find the different versions of a process, providing independent models to describe each of them. In this article, we present an approach that uses the starting time of each process instance as an additional feature to those considered in traditional clustering approaches. By combining control-flow and time features, the clusters formed share both a structural similarity and a temporal proximity. Hence, the process model generated for each cluster should represent a different version of the analyzed business process. A synthetic example set was used for testing, showing the new approach outperforms the basic approach. Although further testing with real data is required, these results motivate us to deepen on this research line.
KeywordsTemporal dimension Clustering Process Mining
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- 2.Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Context Aware Trace Clustering: Towards Improving Process Mining Results. In: Proceedings of the SIAM International Conference on Data Mining, SDM, pp. 401–412 (2009)Google Scholar
- 7.Alves de Medeiros, A.K., Günther, C.W.: Process Mining: Using CPN Tools to Create Test Logs for Mining Algorithms. In: Jensen, K. (ed.) Proceedings of the Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, pp. 177–190 (2005)Google Scholar