NextPriorityConcept: A new and generic algorithm computing concepts from complex and heterogeneous data

Abstract

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 by the pattern structure theory, where data are locally described by predicates independent of their types, allowing the management of heterogeneous data.