Subgroup discovery in process mining

M. Fani Sani, W.M.P. van der Aalst, A.J. Bolt Irondo, J. García-Algarra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
8 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationBusiness Information Systems
Subtitle of host publication20th International Conference, BIS 2017, Poznan, Poland, June 28–30, 2017, Proceedings
EditorsW. Abramowicz
Place of PublicationDordrecht
Number of pages16
ISBN (Electronic)978-3-319-59336-4
ISBN (Print)978-3-319-59335-7
Publication statusPublished - 2017
Event20th International Conference on Business Information Systems, (BIS 2017), 28-30 June 2017, Poznan, Poland - Poznan, Poland
Duration: 28 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


Conference20th International Conference on Business Information Systems, (BIS 2017), 28-30 June 2017, Poznan, Poland
Abbreviated titleBIS 2017
Internet address


  • Pattern mining
  • Performance management
  • Process mining
  • Quality of metrics
  • Subgroup discovery


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