  Extraction of association rules is widely used as a data mining method.
However, one of the limit of this approach comes from the large number of
extracted rules and the difficulty for a human expert to deal with the totality
of these rules. We propose to solve this problem by structuring the set of
rules into hierarchy. The expert can then therefore explore the rules, access
from one rule to another one more general when we raise up in the hierarchy,
and in other hand, or a more specific rules. Rules are structured at two
levels. The global level aims at building a hierarchy from the set of rules
extracted. Thus we define a first type of rule-subsomption relying on Galois
lattices. The second level consists in a local and more detailed analysis of
each rule. It generate for a given rule a set of generalization rules
structured into a local hierarchy. This leads to the definition of a second
type of subsomption. This subsomption comes from inductive logic programming
and integrates a terminological model.
