Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Information Systems

BIS 2017: Business Information Systems pp 237–252Cite as

Subgroup Discovery in Process Mining

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 288))

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016)

    Book  Google Scholar 

  2. Van der Aalst, W.M.P.: Using process mining to bridge the gap between BI and BPM. IEEE Comput. 44(12), 77–80 (2011)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    MATH  Google Scholar 

  6. 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

    Chapter  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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

    Chapter  Google Scholar 

  9. Atzmueller, M.: Subgroup discovery. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 5(1), 35–49 (2015)

    Article  Google Scholar 

  10. Kateri, M.: Contingency Table Analysis. Springer, Heidelberg (2014)

    Book  MATH  Google Scholar 

  11. Herrera, F., et al.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. Atzmueller, M., Baumeister, J., Puppe, F.: Introspective subgroup analysis for interactive knowledge refinement. In: FLAIRS Conference, pp. 402–407 (2006)

    Google Scholar 

  14. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. Knowl. Disc. Databases, 229–238 (1991). https://www.bibsonomy.org/bibtex/26fa5f6987b667b728c7e94f7c68b52d7/enitsirhc

  15. Huynh, X.-H.: Interestingness Measures for Association Rules in a KDD Process: Postprocessing of Rules with ARQAT Tool. Université de Nantes (2006)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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

    Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Mohammadreza Fani Sani, Wil van der Aalst & Alfredo Bolt

  2. Telefonica, Madrid, Spain

    Javier García-Algarra

Authors
  1. Mohammadreza Fani Sani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wil van der Aalst
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfredo Bolt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier García-Algarra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammadreza Fani Sani .

Editor information

Editors and Affiliations

  1. Poznan University of Economics, Poznan, Poland

    Witold Abramowicz

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics

Search

Navigation