Abstract
Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but still have problems discovering and visualizing less structured ones. Unfortunately, process mining is most interesting in domains requiring flexibility. A typical example would be the treatment process in a hospital where it is vital that people can deviate to deal with changing circumstances. Here it is useful to provide insights into the actual processes but at the same time there is a lot of diversity leading to complex models that are difficult to interpret. This paper presents an approach using trace clustering, i.e., the event log is split into homogeneous subsets and for each subset a process model is created. We demonstrate that our approach, based on log profiles, can improve process mining results in real flexible environments. To illustrate this we present a real-life case study.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., Alves de Medeiros, A.K., Song, M., Verbeek, H.M.W.: Business Process Mining: An Industrial Application. Information Systems 32(5), 713–732 (2007)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, New York (2000)
Greco, G., Guzzo, A., Pontieri, L.: Discovering Expressive Process Models by Clustering Log Traces. IEEE Transactions on Knowledge and Data Engineering 18(8), 1010–1027 (2006)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy Mining–Adaptive Process Simplification Based on Multi-Perspective Metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)
Hamming, R.W.: Error Detecting and Error Correcting Codes. Bell System Tech Journal 9, 147–160 (1950)
Li, C., Reichert, M., Wombacher, A.: Discovering Process Reference Models from Process Variants Using Clustering Techniques. Technical Report TR-CTIT-08-30 Centre for Telematics and Information Technology, University of Twente, Enschede (2008)
Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Process Mining in Health Care. In: Azevedo, L., Londral, A.R. (eds.) International Conference on Health Informatics (HEALTHINF 2008), pp. 118–125. IEEE Computer Society, Los Alamitos (2008)
de Medeiros, A.K.A.: Genetic Process Mining. PhD thesis, Eindhoven University of Technology, Eindhoven (2006)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, M., Günther, C.W., van der Aalst, W.M.P. (2009). Trace Clustering in Process Mining. In: Ardagna, D., Mecella, M., Yang, J. (eds) Business Process Management Workshops. BPM 2008. Lecture Notes in Business Information Processing, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00328-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-00328-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00327-1
Online ISBN: 978-3-642-00328-8
eBook Packages: Computer ScienceComputer Science (R0)