Représentations visuelles adaptatives de connaissances associant projection multidimensionnelle (MDS) et analyse de concepts formels (FCA).

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Villerd, Jean (2008) Représentations visuelles adaptatives de connaissances associant projection multidimensionnelle (MDS) et analyse de concepts formels (FCA). Doctorat Informatique Temps réel robotique et automatique, LGI2P - Ecole des Mines d'Alès, ENSMP p.122.

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Résumé

Les outils de recherche d'information sont confrontés à un accroissement constant à la fois du volume et du nombre de dimensions des données accessibles. La traditionnelle liste de résultats ne suffit plus. Un réel besoin en nouvelles techniques de représentation visuelle émerge. Ces nouvelles techniques doivent permettre d'appréhender de manière globale des données nombreuses et multidimensionnelles, en révélant les tendances et la structure générales. On souhaite également pouvoir observer de façon détaillée un ensemble plus restreint de données selon un certain point de vue correspondant à des dimensions particulières.

Notre objectif principal est d'assister l'utilisateur dans sa tâche d'exploration de l'information par une articulation judicieuse entre vue globale et vues locales maintenant sa carte mentale. Pour atteindre cet objectif, nous allions des techniques d'analyse de données capables d'identifier des sous-ensembles pertinents, à des techniques de visualisation d'information permettant de naviguer dynamiquement et intuitivement parmi ces sous-ensembles. Une attention particulière est portée aux problèmes liés aux données manquantes, d'une part, et aux données indexées sur des dimensions mixtes (binaires, nominales, continues), d'autre part.

De plus, conformément aux attentes de la communauté visualisation, nous définissons un cadre formel pour la spécification de visualisations à partir des données à représenter.

Concrètement, nous proposons une méthode de navigation originale associant des techniques de FCA (Formal Concept Analysis) et de visualisation multidimensionnelle MDS (MultiDimensional Scaling). Cette méthode s'appuie sur le paradigme de visualisation "overview + detail" constitué d'une vue globale révélant la structure des données et d'une vue locale affichant les détails d'un élément de la vue globale. Nous tirons parti des propriétés de regroupement du treillis de Galois en l'utilisant comme vue globale pour représenter la structure des données et suggérer des parcours cohérents. La vue locale représente les objets en extension d'un concept sélectionné, projetés par MDS.

Nous illustrons la pertinence de cette méthode sur des données concrètes, issues de nos partenariats industriels, et montrons en quoi les techniques de visualisation liées à FCA et la visualisation spatialisée de données par projection MDS, parfois jugées incompatibles, se révèlent complémentaires.

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