Database Transposition for Constrained (Closed) Pattern Mining

Baptiste Jeudy (1, 2) , François Rioult (3)

1 LHC - Laboratoire Hubert Curien
2 EURISE - Equipe Universitaire de Recherche en Informatique de Saint Etienne
3 Equipe CODAG - Laboratoire GREYC - UMR6072

Recently, different works proposed a new way to mine patterns in databases with pathological size. For example, experiments in genome biology usually provide databases with thousands of attributes (genes) but only tens of objects (experiments). In this case, mining the ''transposed'' database runs through a smaller search space, and the Galois connection allows to infer the closed patterns of the original database. We focus here on constrained pattern mining for those unusual databases and give a theoretical framework for database and constraint transposition. We discuss the properties of constraint transposition and look into classical constraints. We then address the problem of generating the closed patterns of the original database satisfying the constraint, starting from those mined in the ''transposed'' database. Finally, we show how to generate all the patterns satisfying the constraint from the closed ones.

Keywords: Data mining; formal concepts; constrained mining

Domains: Machine Learning [cs.LG]

Baptiste Jeudy, François Rioult. Database Transposition for Constrained (Closed) Pattern Mining. Bart Goethals, Arno Siebes. Knowledge Discovery in Inductive Databases, Third International Workshop, KDID 2004, Pisa, Italy, Septembre 2004, Revised Selected and Invited Papers, Springer, pp.89-107, 2004, LNCS 3377. ⟨ujm-00359284⟩