Optimization of association rule mining queries

Abstract

Levelwise algorithms (e.g., the APRIORI algorithm) have been proved effective for association rule mining from sparse data. However, in many practical applications, the computation turns to be intractable for the user-given 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 efficient use of user defined constraints and the computation of condensed representations for frequent itemsets, e.g., the frequent closed sets. We show that the benefits of these two approaches can be combined into a levelwise algorithm. It can be used for the discovery of association rules in difficult cases (dense and highly-correlated data). For instance, we report an experimental validation related to the discovery of association rules with negations.