Abstract
Process mining enables multiple types of process analysis based on event data. In many scenarios, there are interesting subsets of cases that have deviations or that are delayed. Identifying such subsets and comparing process mining results is a key step in any process mining project.
We aim to find the statistically most interesting patterns of a subset of cases. These subsets can be created by process mining algorithms features (e.g., conformance checking diagnostics) and serve as input for other process mining techniques. We apply subgroup discovery in the process mining domain to generate actionable insights like patterns in deviating cases. Our approach is supported by the ProM framework. For evaluation, an experiment has been conducted using event data from a large Spanish telecommunications company. The results indicate that using subgroup discovery, we could extract interesting insights that could only be found by spitting the event data in the right manner.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016)
Van der Aalst, W.M.P.: Using process mining to bridge the gap between BI and BPM. IEEE Comput. 44(12), 77–80 (2011)
Herrera, F., Carmona, C.J., González, P., Del Jesus, M.J.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)
Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 401–412. SIAM (2009)
Novak, P.K., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)
Verbeek, H.M.W., Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). doi:10.1007/978-3-642-17722-4_5
Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271. American Association for Artificial Intelligence (1996)
Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). doi:10.1007/3-540-63223-9_108
Atzmueller, M.: Subgroup discovery. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 5(1), 35–49 (2015)
Kateri, M.: Contingency Table Analysis. Springer, Heidelberg (2014)
Herrera, F., et al.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)
Duivesteijn, W., et al.: Subgroup discovery meets Bayesian networks-an exceptional model mining approach. 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE (2010)
Atzmueller, M., Baumeister, J., Puppe, F.: Introspective subgroup analysis for interactive knowledge refinement. In: FLAIRS Conference, pp. 402–407 (2006)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. Knowl. Disc. Databases, 229–238 (1991). https://www.bibsonomy.org/bibtex/26fa5f6987b667b728c7e94f7c68b52d7/enitsirhc
Huynh, X.-H.: Interestingness Measures for Association Rules in a KDD Process: Postprocessing of Rules with ARQAT Tool. Université de Nantes (2006)
Song, M., van der Aalst, W.M.P.: Supporting process mining by showing events at a glance. In: Proceedings of the 17th Annual Workshop on Information Technologies and Systems (WITS), pp. 139–145 (2007)
Bolt, A., Leoni, M., Aalst, W.M.P.: A visual approach to spot statistically-significant differences in event logs based on process metrics. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 151–166. Springer, Cham (2016). doi:10.1007/978-3-319-39696-5_10
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Process and deviation exploration with inductive visual miner. In: BPM (Demos), p. 46 (2014)
Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 2(2), 182–192 (2012)
Verbeek, H.M.W., Buijs, J., Van Dongen, B.F., van der Aalst, W.M.P.: Prom 6: the process mining toolkit. Proc. BPM Demonstration Track 615, 34–39 (2010)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference Very Large Databases, VLDB, vol. 1215, pp. 487–499 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fani Sani, M., van der Aalst, W., Bolt, A., García-Algarra, J. (2017). Subgroup Discovery in Process Mining. In: Abramowicz, W. (eds) Business Information Systems. BIS 2017. Lecture Notes in Business Information Processing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-59336-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-59336-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59335-7
Online ISBN: 978-3-319-59336-4
eBook Packages: Computer ScienceComputer Science (R0)