Abstract
Process mining discovers process models from event logs. Logs containing heterogeneous sets of traces can lead to complex process models that try to account for very different behaviour in a single model. Trace clustering identifies homogeneous sets of traces within a heterogeneous log and allows for the discovery of multiple, simpler process models. In this paper, we present a trace clustering method based on local alignment of sequences, subsequent multidimensional scaling, and k-means clustering. We describe its implementation and show that its performance compares favourably to state-of-the-art clustering approaches on two evaluation problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use the terms distance and dissimilarity matrix interchangeably, and also use the term similarity matrix synonymously, as one can cluster equally well by maximal similarity or minimal distance.
- 2.
- 3.
- 4.
We thank one of the anonymous reviewers for this specific example.
References
Thaler, T., Ternis, S.F., Fettke, P., Loos, P.: A comparative analysis of process instance cluster techniques. In: Thomas, O., Teuteberg, F., (eds.) Smart Enterprise Engineering: 12. Internationale Tagung Wirtschaftsinformatik, WI 2015, Osnabrück, Germany, 4–6 March 2015, pp. 423–437 (2015)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)
De Weerdt, J., Vanthienen, J., Baesens, B., et al.: Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25(12), 2708–2720 (2013)
Weijters, A., Ribeiro, J.: Flexible heuristics miner (FHM). In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining CIDM 2011, Paris, France (2011)
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A robust f-measure for evaluating discovered process models. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, Part of the IEEE Symposium Series on Computational Intelligence 11–15 2011, Paris, France, pp. 148–155. IEEE (2011)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162(3), 705–708 (1982)
Cox, T.F., Cox, M.A.: Multidimensional Scaling. CRC Press, Boca Raton (2000)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W.E., Weijters, A.J.M.M.T., van der Aalst, W.M.P.: 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)
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: Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)
Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 2(2), 182–192 (2012)
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013)
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)
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)
Van Dongen, B., Weber, B., Ferreira, D., De Weerdt, J.: Business process intelligence challenge (BPIC 2014) (2014)
Van Dongen, B., Weber, B., Ferreira, D.: Business process intelligence challenge (BPIC 2012) (2012)
Thaler, T., Fettke, P., Loos, P.: Process mining - Fallstudie leginda.de. HMD Praxis der Wirtschaftsinformatik 293, 56–66 (2013)
Melcher, J.: Process Measurement in Business Process Management- Theoretical Framework and Analysis of Several Aspects. KIT Scientific Publishing, Karlsruhe, Germany (2012)
Bose, R.P.J.C., van der Aalst, W.M.P.: Process diagnostics using trace alignment: opportunities, issues, and challenges. Inf. Syst. 37(2), 117–141 (2012)
Bose, R.P.J.C., van der Aalst, W.M.P.: Trace alignment in process mining: opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010)
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)
Bose, R.P.J.C., 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 2009, 30 April–2 May 2009, Sparks, Nevada, USA, pp. 401–412. SIAM (2009)
Sellers, P.H.: On the theory and computation of evolutionary distances. SIAM J. Appl. Math. 26(4), 787–793 (1974)
Ferreira, D.R.: Applied sequence clustering techniques for process mining. In: Cardoso, J., van der Aalst, W. (eds.) Handbook of Research on Business Process Modeling, pp. 481–502. Information Science Reference, Hershey, PA (2009)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Evermann, J., Thaler, T., Fettke, P. (2016). Clustering Traces Using Sequence Alignment. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-42887-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42886-4
Online ISBN: 978-3-319-42887-1
eBook Packages: Computer ScienceComputer Science (R0)