Knowledge discovery from very large databases using frequent concept lattices

Abstract

Data clustering and association rules discovery are two related problems in data mining. In this paper, we propose to integrate these two techniques using the frequent concept lattice data structure - a formal conceptual model that can be used to identify similarities among a set of objects based on their frequent attributes (frequent items). Experimental results show that clusterings and association rules are generated efficiently fromthe frequent concept lattice, since response time after lattice construction is measured almost in seconds.