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A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database

Jean-Philippe Metivier 1 Samir Loudni 1 Thierry Charnois 2, 1
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In the context of sequential pattern mining, a large number of devoted techniques have been developed for solving particular classes of constraints. The aim of this paper is to investigate the use of Constraint Programming (CP) to model and mine sequential patterns in a sequence database. Our CP approach offers a natural way to simultaneously combine in a same framework a large set of constraints coming from various origins. Experiments show the feasibility and the interest of our approach.
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Submitted on : Tuesday, July 15, 2014 - 11:31:18 AM
Last modification on : Saturday, February 15, 2020 - 2:04:40 AM

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  • HAL Id : hal-01023788, version 1
  • ARXIV : 1311.6907


Jean-Philippe Metivier, Samir Loudni, Thierry Charnois. A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database. Int. Workshop Languages for Data Mining and Machine Learningco-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013), Sep 2013, Prague, Czech Republic. p.50-64. ⟨hal-01023788⟩



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