Knowledge-based Sequence Mining with ASP

Martin Gebser 1 Thomas Guyet 2 René Quiniou 2 Javier Romero 3 Torsten Schaub 2, 3
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : We introduce a framework for knowledge-based sequence mining, based on Answer Set Programming (ASP). We begin by modeling the basic task and refine it in the sequel in several ways. First, we show how easily condensed patterns can be extracted by modular extensions of the basic approach. Second, we illustrate how ASP's preference handling capacities can be exploited for mining patterns of interest. In doing so, we demonstrate the ease of incorporating knowledge into the ASP-based mining process. To assess the trade-off in effectiveness, we provide an empirical study comparing our approach with a related sequence mining mechanism.
Type de document :
Communication dans un congrès
IJCAI 2016- 25th International joint conference on artificial intelligence, Jul 2016, New-york, United States. AAAI, Proceedings of the international joint conference on artificial intelligence (IJCAI), pp.8
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01327363
Contributeur : Thomas Guyet <>
Soumis le : lundi 6 juin 2016 - 16:20:42
Dernière modification le : mercredi 16 mai 2018 - 11:24:11

Fichier

Gebser_IJCAI2016.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01327363, version 1

Citation

Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, Torsten Schaub. Knowledge-based Sequence Mining with ASP. IJCAI 2016- 25th International joint conference on artificial intelligence, Jul 2016, New-york, United States. AAAI, Proceedings of the international joint conference on artificial intelligence (IJCAI), pp.8. 〈hal-01327363〉

Partager

Métriques

Consultations de la notice

536

Téléchargements de fichiers

326