On Horn Axiomatizations for Sequential Data
José L. Balcázar and Gemma Casas-Garriga
In: 11th Int. Conference on Database Theory, Edimburgh, Scotland(2004).
We propose a notion of deterministic association rules
for ordered data. We prove that our proposed rules
can be formally justified by a purely logical
characterization, namely, a natural notion of
empirical Horn approximation for ordered data
which involves background Horn conditions; these
ensure the consistency of the propositional theory
obtained with the ordered context. The main proof
resorts to a concept lattice model in the framework
of Formal Concept Analysis, but adapted to ordered contexts.
We also discuss a general method to mine these rules
that can be easily incorporated into any algorithm
for mining closed sequences, of which there are
already some in the literature.