A Conformity Measure using Background Knowledge for Association Rules: Application to Text Mining

Hacène Cherfi 1 Amedeo Napoli 2 Yannick Toussaint 2
2 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A text mining process using association rules generates a very large number of rules. According to experts of the domain, most of these rules basically convey a common knowledge, i.e. rules which associate terms that experts may likely relate to each other. In order to focus on the result interpretation and discover new knowledge units, it is necessary to define criteria for classifying the extracted rules. Most of the rule classification methods are based on numerical quality measures. In this chapter, we introduce two classification methods: The first one is based on a classical numerical approach, i.e. using quality measures, and the other one is based on domain knowledge. We propose the second original approach in order to classify association rules according to qualitative criteria using domain model as background knowledge. Hence, we extend the classical numerical approach in an effort to combine data mining and semantic techniques for post mining and selection of association rules. We mined a corpus of texts in molecular biology and present the results of both approaches, compare them, and give a discussion on the benefits of taking into account a knowledge domain model of the data.
Type de document :
Chapitre d'ouvrage
Yanchang Zhao and Chengqi Zhang and Longbing Cao. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, 2009, 978-1605664040
Liste complète des métadonnées

Littérature citée [19 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00437237
Contributeur : Yannick Toussaint <>
Soumis le : lundi 30 novembre 2009 - 10:18:50
Dernière modification le : jeudi 11 janvier 2018 - 16:42:00
Document(s) archivé(s) le : samedi 26 novembre 2016 - 15:38:13

Fichier

PM06Cherfi-final.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00437237, version 1

Collections

Citation

Hacène Cherfi, Amedeo Napoli, Yannick Toussaint. A Conformity Measure using Background Knowledge for Association Rules: Application to Text Mining. Yanchang Zhao and Chengqi Zhang and Longbing Cao. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, 2009, 978-1605664040. 〈inria-00437237〉

Partager

Métriques

Consultations de la notice

229

Téléchargements de fichiers

99