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Discriminant chronicle mining

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.
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Contributor : Thomas Guyet Connect in order to contact the contributor
Submitted on : Friday, November 30, 2018 - 9:24:24 AM
Last modification on : Tuesday, January 4, 2022 - 10:14:03 AM
Long-term archiving on: : Friday, March 1, 2019 - 1:00:00 PM


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Yann Dauxais, David Gross-Amblard, Thomas Guyet, André Happe. Discriminant chronicle mining. B. Pinaud; F. Guillet; F. Gandon and C. Largeron. Advances in Knowledge Discovery and Management (vol 8), Springer, Cham, pp.89-118, 2019, Advances in Knowledge Discovery and Management, 978-3-030-18128-4. ⟨10.1007/978-3-030-18129-1_5⟩. ⟨hal-01940146⟩



Les métriques sont temporairement indisponibles