Local mining of Association Rules with Rule Schemas
Andrei Olaru, Claudia Marinica, Fabrice Guillet · 2009
One of the central problems in knowledge discovery in databases, more precisely in the field of association rule mining, relies on the very large number of rules that classic rule mining systems extract. This problem is usually solved by means of a post-processing step, that filters the entire volume of extracted rules, in order to output only a few potentially interesting ones. This article presents a new approach that allows the user to explore the rule space locally, incrementally, without the need to extract and post-process all rules in the database. This solution is based on rule schemas, a new formalism designed in order to improve the representation of user beliefs and expectations, and on a novel algorithm for local association rule mining starting from Schemas. The proposed algorithm has been successfully tested on the database provided by Nantes Habitat.