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Mining rare sequential patterns with ASP

Samet, Ahmed; Guyet, Thomas; Negrevergne, Benjamin (2017), Mining rare sequential patterns with ASP, in Lachiche, Nicolas; Vrain, Christel, 27th International Conference on Inductive Logic Programming (ILP 2017), Centre International Universitaire pour la Recherche : Orléans

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Type
Communication / Conférence
Date
2017
Conference title
27th International Conference on Inductive Logic Programming (ILP 2017)
Conference date
2017-09
Conference city
Orléans
Conference country
France
Book title
27th International Conference on Inductive Logic Programming (ILP 2017)
Book author
Lachiche, Nicolas; Vrain, Christel
Publisher
Centre International Universitaire pour la Recherche
Published in
Orléans
Metadata
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Author(s)
Samet, Ahmed
Inria Rennes – Bretagne Atlantique
Guyet, Thomas
Laboratoire d'Informatique - Agrocampus Ouest
Negrevergne, Benjamin cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
This article presents an approach of meaningful rare sequential pattern mining based on the declarative programming paradigm of Answer Set Programming (ASP). The setting of rare sequential pattern mining is introduced. Our ASP approach provides an easy manner to encode expert constraints on expected patterns to cope with the huge amount of meaningless rare patterns. Encodings are presented and quantitatively compared to a procedural baseline. An application on care pathways analysis illustrates the interest of expert constraints encoding.
Subjects / Keywords
rare patterns; sequential patterns; declarative pattern mining; patient care pathways