A best-first strategy for finding frequent sets

The association rule discovery problem consists in identifying frequent itemsets in a database and, then, forming conditional implication rules among them. The algorithmically most difficult part of this task is finding all frequent sets. There exists a wealth of algorithms both for the problem as such and for variations, particular cases, and generalizations. Except for some recent, fully different approaches, most algorithms can be seen either as a breadthfirst search or a depth-first search of the lattice of itemsets. In this paper, we propose a way of developing best-first search strategies