Towards the Tractable Discovery of Association Rules with Negations
Jean‐François Boulicaut, Artur Bykowski, Baptiste Jeudy · Flexible Query Answering Systems · 2001
Frequent association rules (e.g., A∧B⇒C to say that when properties A and B are true in a record then, C tends to be also true) have become a popular way to summarize huge datasets. The last 5 years, there has been a lot of research on association rule mining and more precisely, the tractable discovery of interesting rules among the frequent ones. We consider now the problem of mining association rules that may involve negations e.g., A∧B⇒⌝C or ⌝A∧B⇒C. Mining such rules is difficult and remains an open problem. We identify several possibilities for a tractable approach in practical cases. Among others, we discuss the active use of constraints. We propose a generic algorithm and discuss the use of constraints to mine the generalized sets from which rules with negations can be derived.