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Discriminant Chronicle Mining

  • Yann DauxaisEmail author
  • David Gross-Amblard
  • Thomas Guyet
  • André Happe
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 834)

Abstract

Sequential pattern mining attempts to extract frequent behaviors from a sequential dataset. When sequences are labeled, it is interesting to extract behaviors that characterize each sequence class. This task is called discriminant pattern mining. In this paper, we introduce discriminant chronicle mining. Conceptually, a  chronicle is a temporal graph whose vertices are events and whose edges represent numerical temporal constraints between these events. We propose DCM, an algorithm that mines discriminant chronicles. It is based on rule learning methods that extract the temporal constraints. Computational performances and discriminant power of extracted chronicles are evaluated on synthetic and real data. Finally, we apply this algorithm to the case study consisting in analyzing care pathways of epileptic patients.

Notes

Acknowledgements

This project has been founded by the French Agency of Medicines and Health Products Safety (ANSM). We would like to thank Pr. E. Oger and Pharm.D E. Polard for agreeing to study the patterns extracted from the real dataset.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yann Dauxais
    • 1
    Email author
  • David Gross-Amblard
    • 1
  • Thomas Guyet
    • 2
  • André Happe
    • 3
  1. 1.Rennes University-1/IRISA-UMR 6074RennesFrance
  2. 2.AGROCAMPUS-OUEST/IRISA-UMR 6074RennesFrance
  3. 3.CHRU Brest/EA-7449 REPERESBrestFrance

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