Abstract
Whenever traditional process discovery techniques are confronted with complex and flexible environments, equipping all the traces with just one single model might lead to a spaghetti-like process description. Trace clustering which splits the logs into clusters and applies discovery algorithm per cluster has affirmed to be a versatile solution for that. Nevertheless, most trace clustering techniques are not precise enough due to the indiscriminate treatment on the activities captured in traces. As a result, the impacts of some important activities are reduced and some typical information may be distorted or even lost during comparison. In this paper, we propose a novel trace clustering technique that based on constrained traces alignment and then adapt two appropriate clustering strategies into process mining perspective. And experiments on real-life event logs show that our technique has compelling outperformance in terms of process models complexity and comprehensibility.
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References
Aalst, W.V.D.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
Bose, R.P.J.C., van der Aalst, W.M.P.:: Context aware trace clustering: towards improving process mining results. In: SIAM International Conference on Data Mining, pp. 401–412 (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). https://doi.org/10.1007/978-3-642-12186-9_16
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). https://doi.org/10.1007/978-3-642-15618-2_17
de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M., van Dongen, B.F., Saccà, D.: Process mining based on clustering: a quest for precision. In: ter Hofstede, A., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78238-4_4
Ferreira, D.R.: Applied sequence clustering techniques for process mining. In: Handbook of Research on Business Process Modeling, pp. 492–513 (2009)
García BañUelos, L., Dumas, M., La Rosa, M., De Weerdt, J., Ekanayake, C.C.: Controlled automated discovery of collections of business process models. Inf. Syst. 46, 85–101 (2014)
Greco, G., Guzzo, A., Pontieri, L., Sacca, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18, 1010–1027 (2006)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_24
Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning, vol. 167. Springer, New York (2009). https://doi.org/10.1007/978-0-387-21606-5
von Luxbur, U.: A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007)
Reijers, H.A., Mendling, J.: A study into the factors that influence the understandability of business process models. Trans. Sys. Man Cyber. Part A 41, 449–462 (2011)
Song, M., Günther, C.W., van der 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). https://doi.org/10.1007/978-3-642-00328-8_11
Tsai, Y.T.: The constrained common subsequence problem. Inf. Process. Lett. 88, 173–176 (2003)
Weerdt, J.D., Broucke, S.V., Vanthienen, J., Baesens, B.: Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25, 2708–2720 (2013)
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61672022, Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University under Grant No. XXKZD1604, the Fundamental Research Funds for the Central Universities and Foundation of Graduate Innovation of Shanghai Polytechnic University, and Foundation of Graduate Innovation Center in NUAA under Grant No. kfjj20161601.
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Wang, P., Tan, W., Tang, A., Hu, K. (2018). A Novel Trace Clustering Technique Based on Constrained Trace Alignment. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_7
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DOI: https://doi.org/10.1007/978-3-319-74521-3_7
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