Understanding Spaghetti Models with Sequence Clustering for ProM

  • Gabriel M. Veiga
  • Diogo R. Ferreira
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 43)


The goal of process mining is to discover process models from event logs. However, for processes that are not well structured and have a lot of diverse behavior, existing process mining techniques generate highly complex models that are often difficult to understand; these are called spaghetti models. One way to try to understand these models is to divide the log into clusters in order to analyze reduced sets of cases. However, the amount of noise and ad-hoc behavior present in real-world logs still poses a problem, as this type of behavior interferes with the clustering and complicates the models of the generated clusters, affecting the discovery of patterns. In this paper we present an approach that aims at overcoming these difficulties by extracting only the useful data and presenting it in an understandable manner. The solution has been implemented in ProM and is divided in two stages: preprocessing and sequence clustering. We illustrate the approach in a case study where it becomes possible to identify behavioral patterns even in the presence of very diverse and confusing behavior.


Process Mining Preprocessing Sequence Clustering ProM Markov Chains Event Logs Hierarchical Clustering Process Models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    van Dongen, B., de Medeiros, A.A., Verbeek, H., Weijters, A., van der Aalst, W.: The proM framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Greco, G., Guzzo, A., Pontieri, L., Saccá, D.: Mining expressive process models by clustering workflow traces. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 52–62. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M.T., van Dongen, B.F., Saccà, D.: Process mining based on clustering: A quest for precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Weijters, A., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristicsminer algorithm. BETA Working Paper Series WP 166, Eindhoven University of Technology (2006)Google Scholar
  5. 5.
    Song, M., Günther, C., van der Aalst, W.: Trace clustering in process mining. In: Proceedings of the 4th Workshop on Business Process Intelligence (BPI 2008), BPM Workshops 2008, Milan, September 1 (2008)Google Scholar
  6. 6.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, pp. 401–412. SIAM, Philadelphia (2009)Google Scholar
  7. 7.
    Enright, A.J., Ouzounis, C.: Generage: a robust algorithm for sequence clustering and domain detection. Bioinformatics 16(5), 451–457 (2000)CrossRefGoogle Scholar
  8. 8.
    Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a web site. Data Mining and Knowledge Discovery 7(4), 399–424 (2003)CrossRefGoogle Scholar
  9. 9.
    Ferreira, D.: Applied sequence clustering techniques for process mining. In: Cardoso, J., van der Aalst, W. (eds.) Handbook of Research on Business Process Modeling. IGI Global (2009)Google Scholar
  10. 10.
    Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching process mining with sequence clustering: Experiments and findings. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 360–374. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)Google Scholar
  12. 12.
    Enright, A.J., van Dongen, S., Ouzounis, C.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30(7), 1575–1584 (2002)CrossRefGoogle Scholar
  13. 13.
    Tang, Z., MacLennan, J.: Data Mining with SQL Server 2005, ch. 8 pp. 209–227. Wiley Publishing, Inc., Chichester (2005)Google Scholar
  14. 14.
    van Dongen, B., van der Aalst, W.: A meta model for process mining data. In: Casto, J., Teniente, E. (eds.) Proceedings of the CAiSE 2005 Workshops (EMOI-INTEROP Workshop), FEUP, Porto, Portugal, vol. 2, pp. 309–320 (2005) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gabriel M. Veiga
    • 1
  • Diogo R. Ferreira
    • 1
  1. 1.IST – Technical University of LisbonPorto SalvoPortugal

Personalised recommendations