RCAviz: Visualizing and exploring relational conceptual structures

Muller Emile, Huchard Marianne, Martin Pierre, Poncelet Pascal, Sallaberry Arnaud. 2022. RCAviz: Visualizing and exploring relational conceptual structures. In : Proceedings of the Sixteenth International Conference on Concept Lattices and Their Applications (CLA 2022). Cordero Pablo (ed.), Krídlo Ondrej (ed.). Aachen : CEUR-WS, 133-146. (CEUR Workshop Proceedings, 3308) International Conference on Concept Lattices and their Applications (CLA 2022). 16, Tallinn, Estonie, 20 Juin 2022/22 Juin 2022.

Résumé : Formal Concept Analysis (FCA) aims at classifying a set of objects described by Boolean attributes as a concept lattice. Among the various FCA extensions, Relational Concept Analysis (RCA) supports multiple binary relationships between several sets of objects. Using such relational data, RCA builds interconnected conceptual structures, i.e. concept lattices or AOC-posets. Analyzing data can benefit from exploring these interconnected structures by navigating from one concept to another. Navigation can be performed within the same structure through the classification links or from one concept of a conceptual structure to a concept of another one through one of the relations linking the lattices. This paper presents RCAviz1, an online tool which aims to support such navigation. Once the user has selected a subset of objects and attributes as a starting point for navigation, RCAviz presents the associated concept and its close neighbors. Each concept includes the objects and attributes it introduces. The user can then navigate, i.e. zoom and pan the current view, and move from one concept to another. Additional views show the previous and next conceptual structures. The history allows the user to browse its navigation.

Mots-clés libres : Formal concept analysis, Relational concept analysis, Visualization, Navigation, Concept Lattice

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Institut Convergences en Agriculture Numérique

Auteurs et affiliations

• Muller Emile, Université de Montpellier (FRA)
• Huchard Marianne, Université de Montpellier (FRA)
• Martin Pierre, CIRAD-PERSYST-UPR AIDA (FRA) ORCID: 0000-0002-4874-5795
• Poncelet Pascal, LIRMM (FRA)
• Sallaberry Arnaud, LIRMM (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/603625/)

Métriques

[ Page générée et mise en cache le 2023-09-15 ]