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SECPI: Searching for Explanations for Clustered Process Instances

  • Jochen De Weerdt
  • Seppe vanden Broucke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)

Abstract

This paper presents SECPI (Search for Explanations of Clusters of Process Instances), a technique that assists users with understanding a trace clustering solution by finding a minimal set of control-flow characteristics whose absence would prevent a process instance from remaining in its current cluster. As such, the shortcoming of current trace clustering techniques regarding the provision of insight into the computation of a particular partitioning is addressed by learning concise individual rules that clearly explain why a certain instance is part of a cluster.

Keywords

process discovery trace clustering user comprehension instance-level explanations support vector machines 

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References

  1. 1.
    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)Google Scholar
  2. 2.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: 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)Google Scholar
  3. 3.
    Ferreira, D.R., 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)Google Scholar
  4. 4.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, SIAM, pp. 401–412. SIAM (2009)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction. Data Knowl. Eng. 70(12), 1005–1029 (2011)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Song, M., Yang, H., Siadat, S., Pechenizkiy, M.: A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Systems with Applications 40(9), 3722–3737 (2013)Google Scholar
  9. 9.
    Ekanayake, C.C., Dumas, M., García-Bañuelos, L., La Rosa, M.: Slice, mine and dice: Complexity-aware automated discovery of business process models. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 49–64. Springer, Heidelberg (2013)Google Scholar
  10. 10.
    Martens, D., Provost, F.: Explaining data-driven document classifications. MISQ 38(1), 73–99 (2014)Google Scholar
  11. 11.
    Bellman, R.E.: Adaptive control processes - A guided tour. Princeton University Press (1961)Google Scholar
  12. 12.
    Aggarwal, C., Hinneburg, A., Keim, D.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2000)Google Scholar
  13. 13.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jochen De Weerdt
    • 1
  • Seppe vanden Broucke
    • 1
  1. 1.Research Centre for Management Informatics (LIRIS)KU LeuvenLeuvenBelgium

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