Quantitative Evaluation of Process Discovery Enhancement Using Clustering-Based Techniques

Abstract:

Business process discovery aims to reconstruct accurate models of organizational workflowsfrom event logs, yet classical algorithms often struggle with noisy and heterogeneous data,producing overly complex or imprecise models. This paper presents a systematic, quantitative evaluationof clustering-based preprocessing as a means to enhance process discovery. Using the BPIChallenge 2014 incident management log, we applied comprehensive feature engineering to extractstructural, temporal, and behavioral attributes, followed by multiple clustering techniques (K-Means,DBSCAN, HDBSCAN, GMM). Within each cluster, four established discovery algorithms (Alpha Miner,Heuristic Miner, Inductive Miner, ILP Miner) were executed from the PM4Py Python libraryand evaluated using fitness, precision, generalization, and simplicity metrics. Results show that clusteringconsistently improved model simplicity and precision, while fitness remained stable and generalizationtended to decrease. The extent of improvement depended on both the clustering methodand the discovery algorithm: K-Means with higher cluster numbers enhanced fitness and simplicityfor Alpha Miner; HDBSCAN improved precision and simplicity for Heuristic Miner; InductiveMiner benefited from K-Means even with small cluster numbers; whereas ILP Miner models remainedhighly complex regardless of clustering. These findings highlight the trade-off between interpretabilityand generalization, and demonstrate that clustering-based preprocessing can yield moretransparent and actionable process models.

• Periodical: Advances in Science and Technology (Volume 181)
• Pages: 435-450
• DOI: https://doi.org/10.4028/p-3gEYNN
• Online since: August 2026
• Authors: Erika Baksáné Varga*, Attila Baksa
• Keywords: Business Process Modeling, Clustering, Machine Learning, PM4Py, Process Discovery, Process Mining

References

[1] W. M. P. van der Aalst. Process Mining: Data Science in Action. Springer, 2016.

[2] M. Dumas, M. La Rosa, J. Mendling, and H. A. Reijers. Fundamentals of Business Process Management. Springer, 2018.

[3] A. Augusto, R. Conforti, M. Dumas, M. La Rosa, F. M. Maggi, A. Marrella, and I. Weber. Automated discovery of process models from event logs: Review and benchmark. Transactions on Knowledge and Data Engineering, 31(4):686–705, 2019.

[4] S.J.J. Leemans, D. Fahland, and W.M.P. van der Aalst. Discovering block-structured process models from event logs containing infrequent behaviour. In Song M. Wohed P. Lohmann, N., editor, Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, volume 171. Springer, Cham., 2013.

DOI: 10.1007/978-3-319-06257-0_6

[5] C. Di Francescomarino, M. Dumas, F. M. Maggi, and I. Teinemaa. Clustering-based predictive process monitoring. IEEE Transactions on Services Computing, 12(6):896–909, 2016.

DOI: 10.1109/tsc.2016.2645153

[6] G. Greco, A. Guzzo, L. Pontieri, and D. Sacca. Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering, 18(8):1010–1027, 2006.

DOI: 10.1109/tkde.2006.123

[7] J. Evermann, J.R. Rehse, and P. Fettke. Predicting process behaviour using deep learning. Decision Support Systems, 100:129–140, 2017.

DOI: 10.1016/j.dss.2017.04.003

[8] P. Pfeiffer, L. Abb, and P. Fettke. Learning from the data to predict the process. Business & Information Systems Engineering, 67:357–383, 2025.

DOI: 10.1007/s12599-025-00936-4

[9] S. Weinzierl, S. Zilker, S. Dunzer, and M. Matzner. Machine learning in business process management: A systematic literature review. In Expert Systems with Applications, volume 253, 2024.

DOI: 10.1016/j.eswa.2024.124181

[10] J.C.A.M. Buijs, B.F. van Dongen, and W.M.P. van der Aalst. Quality dimensions in process discovery: the importance of fitness, precision, generalization and simplicity. International Journal of Cooperative Information Systems, 23(1):1440001, 2014.

DOI: 10.1142/s0218843014400012

[11] W. M. P. van der Aalst. Process mining manifesto. In Barkaoui K. Dustdar S. Daniel, F., editor, Business Process Management Workshops. BPM 2011, volume 99. Springer, Berlin, Heidelberg, 2011.

[12] B. F. van Dongen. BPI Challenge 2014: Activity log for incidents. TU.ResearchData, Eindhoven University of Technology, 2014.

[13] A. J. M. M. Weijters and J. Ribeiro. Flexible heuristics miner (fhm). In IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pages 310–317. Paris, France, 2011.

DOI: 10.1109/cidm.2011.5949453

[14] S. J. van Zelst, B. F. van Dongen, and W. M. P. van der Aalst. Ilp-based process discovery using hybrid regions. In CEUR Workshop Proceedings, volume 1371, pages 47–61, 2015.

[15] J.-Y. Jung, J. Bae, and L. Liu. Hierarchical business process clustering. In IEEE International Conference on Services Computing, pages 613–616. Honolulu, HI, USA, 2008.

DOI: 10.1109/scc.2008.69

[16] P. De Koninck, S. vanden Broucke, and J. De Weerdt. Act2vec, trace2vec, log2vec, and model2vec: Representation learning for business processes. In Montali M. Weber I. vom Brocke J. Weske, M., editor, Business Process Management. BPM 2018. Lecture Notes in Computer Science, volume 11080. Springer, Cham., 2018.

DOI: 10.1007/978-3-319-98648-7_18

[17] A. Seeliger, S. Luettgen, T. Nolle, and M. Mühlhäuser. Learning of process representations using recurrent neural networks. In In Advanced Information Systems Engineering: 33rd International Conference, volume CAiSE 2021. Springer-Verlag, Berlin, Heidelberg, 2021.

[18] J. Buijs, B. F. van Dongen, and W. M. P. van der Aalst. A genetic algorithm for discovering process trees. In IEEE Congress on Evolutionary Computation, pages 1–8. Brisbane, QLD, Australia, 2012.

DOI: 10.1109/cec.2012.6256458

[19] van der Aalst W.M.P. Berti, A. A novel token-based replay technique to speed up conformance checking and process enhancement. In Kordon F. Pomello L. Koutny, M., editor, Transactions on Petri Nets and Other Models of Concurrency XV. Lecture Notes in Computer Science, volume 12530. Springer, Berlin, Heidelberg, 2021.

DOI: 10.1007/978-3-662-63079-2_1

[20] A. Rozinat and W.M.P. van der Aalst. Conformance checking of processes based on monitoring real behavior. Information Systems, 33(1):64–95, 2008.

DOI: 10.1016/j.is.2007.07.001

[21] W.M.P. van der Aalst, A. Adriansyah, and B. van Dongen. Replaying history on process models for conformance checking and performance analysis. In Rev. Data Min. and Knowl, volume Disc 2, pages 182–192. Wiley Int., 2012.

DOI: 10.1002/widm.1045