FCAvizIR: Exploring Relational Data Set’s Implications Using Metrics and Topics - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Conference Papers Year : 2024

FCAvizIR: Exploring Relational Data Set’s Implications Using Metrics and Topics

Abstract

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.
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Dates and versions

lirmm-04668541 , version 1 (06-10-2024)

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Lola Musslin, Alexandre Bazin, Marianne Huchard, Pierre Martin, Pascal Poncelet, et al.. FCAvizIR: Exploring Relational Data Set’s Implications Using Metrics and Topics. CONCEPTS 2024 - 1st International Joint Conference on Conceptual Knowledge Structures, Universidad de Cádiz, Sep 2024, Cadiz, Spain. pp.132-148, ⟨10.1007/978-3-031-67868-4_10⟩. ⟨lirmm-04668541⟩
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