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Detecting Changes in Process Behavior Using Comparative Case Clustering

  • B. F. A. HompesEmail author
  • J. C. A. M. Buijs
  • Wil M. P. van der Aalst
  • P. M. Dixit
  • J. Buurman
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 244)

Abstract

Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights.

Keywords

Process mining Trace clustering Concept drift Process comparison 

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • B. F. A. Hompes
    • 1
    • 2
    Email author
  • J. C. A. M. Buijs
    • 1
  • Wil M. P. van der Aalst
    • 1
  • P. M. Dixit
    • 1
    • 2
  • J. Buurman
    • 2
  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Philips ResearchEindhovenThe Netherlands

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