Advertisement

Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time

  • Daniela Luengo
  • Marcos Sepúlveda
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 99)

Abstract

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.

Keywords

Temporal dimension Clustering Process Mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining: a research agenda. Computers and Industry 53, 231–244 (2004)CrossRefGoogle Scholar
  2. 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
  3. 3.
    Veiga, G.M., Ferreira, D.R.: Understanding Spaghetti Models with Sequence Clustering for ProM. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 92–103. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace Clustering in Process Mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling Concept Drift in Process Mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniela Luengo
    • 1
  • Marcos Sepúlveda
    • 1
  1. 1.Computer Science Department School of EngineeringPontificia Universidad Católica de ChileMaculChile

Personalised recommendations