Mining Rare Association Rules

Laszlo Szathmary 1 Sandy Maumus 1 Amedeo Napoli
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we address the problem of generating relevant rare association rules. In the literature, this problem has not yet been studied in detail, although rare association rules can also contain important information just as frequent association rules do. Our work is motivated by the long-standing open question of devising an efficient algorithm for finding rules with low support and very high confidence. In order to find such rules using conventional frequent itemset mining algorithms like Apriori, the minimum support must be set very low, which drastically increases the runtime of the algorithm. Moreover, when minimum support is set very low, Apriori produces a huge number of frequent itemsets. This is also known as the "rare item problem". For this long-existing problem we propose a solution. A particularly relevant field for rare itemsets and rare association rules is medical diagnosis. For example it may be that in a large group of patients diagnosed with the same sickness, a few patients exhibit unusual symptoms. It is important for the doctor to take this fact into consideration.
Type de document :
Rapport
[Research Report] 2006, pp.19
Liste complète des métadonnées

Littérature citée [24 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00102909
Contributeur : Laszlo Szathmary <>
Soumis le : vendredi 6 octobre 2006 - 23:25:13
Dernière modification le : jeudi 11 janvier 2018 - 06:19:53
Document(s) archivé(s) le : vendredi 25 novembre 2016 - 12:11:27

Fichier

 Accès restreint
Fichier visible le : jamais

Connectez-vous pour demander l'accès au fichier

Identifiants

  • HAL Id : inria-00102909, version 1

Collections

Citation

Laszlo Szathmary, Sandy Maumus, Amedeo Napoli. Mining Rare Association Rules. [Research Report] 2006, pp.19. 〈inria-00102909〉

Partager

Métriques

Consultations de la notice

241