FCAvizIR: Exploring relational data set's implications using metrics and topics

Musslin Lola, Bazin Alexandre, Huchard Marianne, Martin Pierre, Poncelet Pascal, Raveneau Vincent, Sallaberry Arnaud. 2024. FCAvizIR: Exploring relational data set's implications using metrics and topics. In : Conceptual knowledge structures. Cabrera Inma P. (ed.), Ferré Sébastien (ed.), Obiedkov Sergei (ed.). Cham : Springer, 132-148. (Lecture Notes in Computer Science, 14914) International Joint Conference on Conceptual Knowledge Structures (CONCEPTS 2024). 1, Cadiz, Espagne, 9 Septembre 2024/13 Septembre 2024.

Résumé : Implication is a core notion of Formal Concept Analysis and its extensions. It provides information about the regularities present in the data. When one considers a relational data set of real-size, implications are numerous and their formulation, which combines primitive and relational attributes computed using Relational Concept Analysis framework, is complex. For an expert wishing to answer a question based on such a corpus of implications, having a smart exploration strategy is crucial. In this paper, we propose a visual approach, implemented in a web platform named FCAvizIR, for leveraging such corpus. Comprised of three interactive and coordinated views and a toolbox, FCAvizIR has been designed to explore corpora of implication rules following Schneiderman's famous mantra “overview first, zoom and filter, then details on demand”. It enables metrics filtering, e.g. fixing a minimum and a maximum support value, and the multiple selection of relations and attributes in the premise and in the conclusion to identify the corresponding subset of implications presented as a list and Euler diagrams. An example of exploration is presented using an excerpt of Knomana to analyze plant-based extracts for controlling pests.

Mots-clés libres : Formal concept analysis, Relational concept analysis, Data visualization, Visual analytics, Implication rules

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Institut Convergences en Agriculture Numérique, (FRA) Analyse Formelle de Concepts : un outil intelligent pour l'analyse de données complexes

Auteurs et affiliations

• Musslin Lola, Université de Montpellier (FRA)
• Bazin Alexandre, Université de Montpellier (FRA)
• Huchard Marianne, Université de Montpellier (FRA)
• Martin Pierre, CIRAD-PERSYST-UPR AIDA (FRA) ORCID: 0000-0002-4874-5795
• Poncelet Pascal, Université de Montpellier (FRA)
• Raveneau Vincent, Université de Montpellier (FRA)
• Sallaberry Arnaud, UM3 (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/610801/)

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