  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.
