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International Conference on Business Process Management

BPM 2016: Business Process Management Workshops pp 49–60Cite as

Multi-objective Trace Clustering: Finding More Balanced Solutions

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 281))

Abstract

In recent years, a multitude of techniques has been proposed for the task of clustering traces. In general, these techniques either focus on optimizing their solution based on a certain type of similarity between the traces, such as the number of insertions and deletions needed to transform one trace into another; by mapping the traces onto a vector space model, based on certain patterns in each trace; or on the quality of a process model discovered from each cluster. Currently, the main technique of the latter category, ActiTraC, constructs its clusters based on a single objective: fitness. However, a typical view in process discovery is that one needs to balance fitness, generalization, precision and simplicity. Therefore, a multi-objective approach to trace clustering is deemed more appropriate. In this paper, a thorough overview of current trace clustering techniques and potential approaches for multi-objective trace clustering is given. Furthermore, a multi-objective trace clustering technique is proposed. Our solution is shown to provide unique results on a number of real-life event logs, validating its existence.

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Notes

  1. 1.

    The approach is implemented as a ProM 6-plugin, which can be found on http://www.processmining.be/multiobjective.

  2. 2.

    The second and third methods are implemented in the ProM-framework for process mining in the ActiTrac-plugin. The latter three methods can be found in the GuideTree-Miner-plugin.

References

  1. Appice, A., Malerba, D.: A co-training strategy for multiple view clustering in process mining. IEEE Trans. Serv. Comput. PP(99), 1 (2015)

    Google Scholar 

  2. Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of 19th International Conference on Machine Learning, ICML-2002 (2002)

    Google Scholar 

  3. Bose, R., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: Sdm, pp. 401–412 (2009)

    Google Scholar 

  4. Bose, R.P.J.C., 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). doi:10.1007/978-3-642-12186-9_16

    Chapter  Google Scholar 

  5. Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: Discovering and navigating a collection of process models using multiple quality dimensions. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 3–14. Springer, Cham (2014). doi:10.1007/978-3-319-06257-0_1

    Chapter  Google Scholar 

  6. De Koninck, P., De Weerdt, J.: Determining the number of trace clusters: a stability-based approach. In: ATAED 2016, vol. 1592, pp. 1–15. Ceur Workshop Proceedings (2016)

    Google Scholar 

  7. De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)

    Article  Google Scholar 

  8. De Weerdt, J., Vanden Broucke, S., Vanthienen, J., Baesens, B.: Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25(12), 2708–2720 (2013)

    Article  Google Scholar 

  9. Delias, P., Doumpos, M., Grigoroudis, E., Manolitzas, P., Matsatsinis, N.: Supporting healthcare management decisions via robust clustering of event logs. Knowl. Based Syst. 84, 203–213 (2015)

    Article  Google Scholar 

  10. Ekanayake, C.C., Dumas, M., García-Bañuelos, L., 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). doi:10.1007/978-3-642-40176-3_6

    Chapter  Google Scholar 

  11. 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). doi:10.1007/978-3-540-75183-0_26

    Chapter  Google Scholar 

  12. Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Editorial: mining usage scenarios in business processes: outlier-aware discovery and run-time prediction. Data Knowl. Eng. 70, 1005–1029 (2011)

    Article  Google Scholar 

  13. Fred, A., Lourenço, A.: Cluster ensemble methods: from single clusterings to combined solutions. In: Okun, O., Valentini, G. (eds.) Supervised and Unsupervised Ensemble Methods and Their Applications. SCI, vol. 126, pp. 3–30. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Alves de Medeiros, A.K.: Genetic process mining (2006)

    Google Scholar 

  16. Song, M., Günther, C.W., 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). doi:10.1007/978-3-642-00328-8_11

    Chapter  Google Scholar 

  17. van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2012)

    Article  Google Scholar 

  18. Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained k-means clustering with background knowledge. In: In ICML, pp. 577–584. Morgan Kaufmann (2001)

    Google Scholar 

  19. Weijters, A., van der Aalst, W.M.P., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report WP 166, pp. 1–34 (2006)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Economics and Business, Research Center for Management Informatics, KU Leuven, Leuven, Belgium

    Pieter De Koninck & Jochen De Weerdt

Authors
  1. Pieter De Koninck
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  2. Jochen De Weerdt
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Correspondence to Pieter De Koninck .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Marlon Dumas

  2. School of Arts, Sciences and Humanities, University of São Paulo, Sao Paulo, São Paulo, Brazil

    Marcelo Fantinato

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De Koninck, P., De Weerdt, J. (2017). Multi-objective Trace Clustering: Finding More Balanced Solutions. In: Dumas, M., Fantinato, M. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-58457-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-58457-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58456-0

  • Online ISBN: 978-3-319-58457-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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