Optimization of association rule mining queries

Levelwise algorithms (e.g., the Apriori algorithm) have been proved eective for association rule mining from sparse data. However, in many practical applications, the computation turns to be intractable for the usergiven frequency threshold and the lack of focus leads to huge collections of frequent itemsets. To tackle these problems, two promising issues have been investigated during the last four years: the ecient use of user dened constraints and the computation of condensed representations for frequent itemsets, e.g., the frequent closed sets. We show that the benets of these two approaches can be combined into a levelwise algorithm. It can be used for the discovery of association rules in dicult cases (dense and highlycorrelated data). For instance, we report an experimental validation related to the discovery of association rules with negations.

• Type: Journal
• Year: 2002
• Where: IDA
• Authors: Baptiste Jeudy, Jean-François Boulicaut
• Keywords: Algorithm, Association Rules, Frequent Itemsets, IDA 2002, Information Technology