Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data
This work addresses the challenge of concept lattice computation for researchers in data mining and knowledge discovery, though it appears incremental as an extension of existing methods.
The authors tackled the problem of generating concept lattices from heterogeneous and complex data by introducing the NextPriorityConcept algorithm, which extends Bordat's algorithm with strategies to select only some predecessors, avoiding large lattices and handling any data type generically.
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.