Association rule mining is a methodology for discovering
interesting relationships between attributes based on patterns of
their values attained by objects with those attributes.

Formal Concept Analysis involves finding natural clusters of
objects having a given set of attributes, and natural clusters of
attributes of a given set of objects. Pairs for such clusters are
called concepts,  and a family of such concepts forms a Galois
lattice.

In this talk I will explain the relationship, discovered in the
1990s, between association rule mining and formal concept
analysis, and illustrate how this improves the association rule
mining in the case where the objects are sequences. Finally, I
will suggest a framework for incorporating more sophisticated
clustering algorithms.