Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks

Thomas Guyet 1, 2 Yves Moinard 3, 2 René Quiniou 3, 2 Torsten Schaub 4, 2
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA_D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.
Type de document :
Chapitre d'ouvrage
Bruno Pinaud; Fabrice Guillet; Bruno Cremilleux; Cyril de Runz. Advances in Knowledge Discovery and Management, 7, Springer, pp.41--81, 2017, 978-3-319-65405-8
Liste complète des métadonnées

https://hal.inria.fr/hal-01631879
Contributeur : Thomas Guyet <>
Soumis le : lundi 13 novembre 2017 - 17:01:06
Dernière modification le : mercredi 16 mai 2018 - 11:24:14
Document(s) archivé(s) le : mercredi 14 février 2018 - 12:34:09

Identifiants

  • HAL Id : hal-01631879, version 1
  • ARXIV : 1711.05090

Citation

Thomas Guyet, Yves Moinard, René Quiniou, Torsten Schaub. Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks. Bruno Pinaud; Fabrice Guillet; Bruno Cremilleux; Cyril de Runz. Advances in Knowledge Discovery and Management, 7, Springer, pp.41--81, 2017, 978-3-319-65405-8. 〈hal-01631879〉

Partager

Métriques

Consultations de la notice

264

Téléchargements de fichiers

36