Using Answer Set Programming for pattern mining

Thomas Guyet 1, 2 Yves Moinard 1 René Quiniou 1
1 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.
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https://hal.inria.fr/hal-01069092
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Submitted on : Friday, September 26, 2014 - 10:05:57 PM
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  • HAL Id : hal-01069092, version 1
  • ARXIV : 1409.7777

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Thomas Guyet, Yves Moinard, René Quiniou. Using Answer Set Programming for pattern mining. Intelligence Artificielle Fondamentale, Jun 2014, Angers, France. ⟨hal-01069092⟩

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