Relational concept analysis in practice: Capitalizing on data modeling using design patterns

Braud Agnès, Dolques Xavier, Huchard Marianne, Le Ber Florence, Martin Pierre. 2023. Relational concept analysis in practice: Capitalizing on data modeling using design patterns. In : Formal concept analysis: 17th International Conference, ICFCA 2023, Kassel, Germany, July 17–21, 2023, Proceedings. Durrschnabel Dominik (ed.), López Rodríguez Domingo (ed.). Cham : Springer, 166-182. (Lecture Notes in Computer Science, 13934) ISBN 978-3-031-35948-4 International Conference on Formal Concept Analysis (ICFCA 2023). 17, Kassel, Allemagne, 17 Juillet 2023/21 Juillet 2023.

https://doi.org/10.1007/978-3-031-35949-1_12

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Résumé : Many applications of Formal Concept Analysis (FCA) and its diverse extensions have been carried out in recent years. Among these extensions, Relational Concept Analysis (RCA) is one approach for addressing knowledge discovery in multi-relational datasets. Applying RCA requires stating a question of interest and encoding the dataset into the input RCA data model, i.e. an Entity-Relationship model with only Boolean attributes in the entity description and unidirectional binary relationships. From the various concrete RCA applications, recurring encoding patterns can be observed, that we aim to capitalize taking software engineering design patterns as a source of inspiration. This capitalization work intends to rationalize and facilitate encoding in future RCA applications. In this paper, we describe an approach for defining such design patterns, and we present two design patterns: “Separate/Gather Views” and “Level Relations”.

Mots-clés libres : Formal concept analysis, Relational concept analysis, Design pattern, Classification (information)

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Analyse Formelle de Concepts : un outil intelligent pour l'analyse de données complexes

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