  In this article, we present a new data type agnostic algorithm calculating a
concept lattice from heterogeneous and complex data. Our NextPriorityConcept
algorithm is first introduced and proved in the binary case as an extension of
Bordat's algorithm with the notion of strategies to select only some
predecessors of each concept, avoiding the generation of unreasonably large
lattices. The algorithm is then extended to any type of data in a generic way.
It is inspired from pattern structure theory, where data are locally described
by predicates independent of their types, allowing the management of
heterogeneous data.
