Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data

TL;DR

This paper introduces NextPriorityConcept, a versatile algorithm that efficiently computes concept lattices from complex, heterogeneous data by extending existing methods and leveraging pattern structure theory.

Contribution

It presents a new generic algorithm for concept lattice computation applicable to various data types, improving efficiency by strategic predecessor selection.

Findings

Algorithm extends Bordat's method with strategies for large lattices

Successfully handles heterogeneous data through pattern structure theory

Proven in binary case and extended to general data types

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