Abstract
In this article, we recall the NextPriorityConcept algorithm we developed to study concept lattices using first-order monadic predicates. This new approach unifies and simplifies the pattern structure theory proposing to immerse context objects in a dedicated predicate space having the properties of an inference system. This way of managing objects and attributes (monadic predicates) joins the concepts developed in the theory of generalized convex structures, in particular that of half-spaces. We show how this paradigm can be used for boolean, categorized, numerical, character string and sequential data on well-known examples of literature in order to generate lattices whose size is controlled by the user’s choices.
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Notes
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The Iris dataset scatterplot has been taken from https://commons.wikimedia.org/wiki/File:Iris_dataset_scatterplot.svg.
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Bertet, K., Demko, C., Boukhetta, S., Richard, J., Faucher, C. (2022). Analysis of Complex and Heterogeneous Data Using FCA and Monadic Predicates. In: Missaoui, R., Kwuida, L., Abdessalem, T. (eds) Complex Data Analytics with Formal Concept Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-93278-7_4
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