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A Trace Clustering Solution Based on Using the Distance Graph Model

  • Quang-Thuy Ha
  • Hong-Nhung Bui
  • Tri-Thanh Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

Process discovery is the most important task in the process mining. Because of the complexity of event logs (i.e. activities of several different processes are written into the same log), the discovered process models may be diffuse and unintelligible. That is why the input event logs should be clustered into simpler event sub-logs. This work provides a trace clustering solution based on the idea of using the distance graph model for trace representation. Experimental results proved the effect of the proposed solution on two measures of Fitness and Precision, especially the effect on the Precision measure.

Keywords

Event log Process mining Fitness measure Precision measure Process discovering Trace clustering Distance graph model 

Notes

Acknowledgments

This work was supported in part by VNU Grant QG-15- 22.

References

  1. 1.
    van der Aalst, W.M., van Dongen, B.F.: Discovering workflow performance models from timed logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  3. 3.
    Aggarwal, C.C., Zhao, P.: Towards graphical models for text processing. Knowl. Inf. Syst. 36(1), 1–21 (2013)CrossRefGoogle Scholar
  4. 4.
    Bose, R.C., van der Aalst, W.M.: 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
  5. 5.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: SDM 2009, pp. 401–412 (2009)Google Scholar
  6. 6.
    Bose, R.P.J.C.: Process Mining in the Large: Preprocessing, Discovery, and Diagnostics. Ph.D. thesis. Eindhoven University of Technology (2012)Google Scholar
  7. 7.
    Dai, Xin-Yu., Cheng, C., Huang, S., Chen, J.: Sentiment classification with graph sparsity regularization. In: Gelbukh, A. (ed.). LNCS, vol. 9042, pp. 140–151. Springer, Heidelberg (2015)Google Scholar
  8. 8.
    Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)CrossRefGoogle Scholar
  9. 9.
    de Medeiros, A.K.A., van Dongen, B.F., van der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining: extending the alpha-algorithm to mine short loops. BETA Working Paper Series (2004)Google Scholar
  10. 10.
    de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M., Weijters, A., van Dongen, B.F., Saccà, D.: Process mining based on clustering: a quest for precision. In: Hofstede, A.H., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Rozinat, A., van der Wil, M.P.: Aalst. Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  12. 12.
    Buijs, J.C., van Dongen, B.F., van der Aalst, W.M.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., Panetto, H., Dillon, T., Rinderle-Ma, S., Dadam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., Cruz, I.F. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Song, M., Günther, C.W., van der Aalst, W.M.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) Business Process Management Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Thaler, T., Ternis, S.F., Fettke, P., Loos, P.: A comparative analysis of process instance cluster techniques. In: Wirtschaftsinformatik 2015, pp. 423–437 (2015)Google Scholar
  15. 15.
    De Weerdt, J., van den Broucke, S.K.L.M., Vanthienen, J., Baesens, B.: Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)Google Scholar
  16. 16.
    De Weerdt, J., vanden Broucke, S.K.L.M., Vanthienen, J., Baesens, B.: Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25(12), 2708–2720 (2013)CrossRefGoogle Scholar
  17. 17.
    Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Discov. 15(2), 145–180 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Deza, M.M., Deza, E.: Distances in Graph Theory. Springer, Heidelberg (2014)CrossRefMATHGoogle Scholar
  19. 19.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Quang-Thuy Ha
    • 1
  • Hong-Nhung Bui
    • 1
    • 2
  • Tri-Thanh Nguyen
    • 1
  1. 1.Vietnam National University (VNU), VNU-University of Engineering and Technology (UET)HanoiVietnam
  2. 2.Banking Academy of VietnamHanoiVietnam

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