Abstract
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”.
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This work was supported by the ANR SmartFCA project, Grant ANR-21-CE23-0023 of the French National Research Agency.
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Braud, A., Dolques, X., Huchard, M., Le Ber, F., Martin, P. (2023). Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns. In: Dürrschnabel, D., López Rodríguez, D. (eds) Formal Concept Analysis. ICFCA 2023. Lecture Notes in Computer Science(), vol 13934. Springer, Cham. https://doi.org/10.1007/978-3-031-35949-1_12
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