Pruning closed itemset lattices for association rules

Nicolas Pasquier (1) , Yves Bastide (1) , Rafik Taouil (1) , Lotfi Lakhal (1)

1 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes

Discovering association rules is one of the most important task in data mining and many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning of the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning of the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census data, and performs reasonably well for market basket style data.

Domaines: Algorithme et structure de données [cs.DS]; Base de données [cs.DB]

Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lotfi Lakhal. Pruning closed itemset lattices for association rules. BDA'1998 international conference on Advanced Databases, Oct 1998, Hammamet, Tunisia. pp.177-196. ⟨hal-00467745⟩