Mining association rules using frequent closed itemsets

Abstract : In the domain of knowledge discovery in databases and its computational part called data mining, many works addressed the problem of association rule extraction that aims at discovering relationships between sets of items (binary attributes). An example association rule fitting in the context of market basket data analysis is cereal ∧ milk → sugar (support 10%, confidence 60%). This rule states that 60% of customers who buy cereals and sugar also buy milk, and that 10% of all customers buy all three items. When an association rule support and confidence exceed some user-defined thresholds, the rule is considered relevant to support decision making. Association rule extraction has proved useful to analyze large databases in a wide range of domains, such as marketing decision support, diagnosis and medical research support, telecommunication process improvement, Web site management and profiling, spatial, geographical, and statistical data analysis, and so forth.
Type de document :
Chapitre d'ouvrage
Encyclopedia of Data Warehousing and Mining, Information Science Reference, pp.Volume 2, 2005
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00363019
Contributeur : Nicolas Pasquier <>
Soumis le : dimanche 25 avril 2010 - 17:01:23
Dernière modification le : dimanche 25 avril 2010 - 20:27:31
Document(s) archivé(s) le : mardi 14 septembre 2010 - 17:03:02

Fichier

Pasquier_-_2005_-_Mining_assoc...
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : hal-00363019, version 1

Collections

Citation

Nicolas Pasquier. Mining association rules using frequent closed itemsets. Encyclopedia of Data Warehousing and Mining, Information Science Reference, pp.Volume 2, 2005. <hal-00363019>

Partager

Métriques

Consultations de
la notice

114

Téléchargements du document

140