Mining association rules using frequent closed itemsets

Nicolas Pasquier (1)

1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

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.

Mots clés: Data Mining; Frequent Closed Itemsets; Generators; Association Rules

Domaines: Algorithme et structure de données [cs.DS]; Base de données [cs.DB]; Autre [cs.OH]

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