Mining Periodic Patterns with a MDL Criterion

Esther Galbrun 1 Peggy Cellier 2 Nikolaj Tatti 1, 3 Alexandre Termier 4 Bruno Crémilleux 5, 6
2 SemLIS - Semantics, Logics, Information Systems for Data-User Interaction
IRISA_D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
4 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA_D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
5 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : The quantity of event logs available is increasing rapidly, be they produced by industrial processes, computing systems, or life tracking , for instance. It is thus important to design effective ways to uncover the information they contain. Because event logs often record repetitive phenomena, mining periodic patterns is especially relevant when considering such data. Indeed, capturing such regularities is instrumental in providing condensed representations of the event sequences. We present an approach for mining periodic patterns from event logs while relying on a Minimum Description Length (MDL) criterion to evaluate candidate patterns. Our goal is to extract a set of patterns that suitably characterises the periodic structure present in the data. We evaluate the interest of our approach on several real-world event log datasets.
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Submitted on : Tuesday, December 11, 2018 - 3:53:13 PM
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Esther Galbrun, Peggy Cellier, Nikolaj Tatti, Alexandre Termier, Bruno Crémilleux. Mining Periodic Patterns with a MDL Criterion. ECML/PKDD 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2018, Dublin, Ireland. pp.535-551. ⟨hal-01951722⟩

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