Relational concept discovery in structured datasets

Relational datasets, i.e., datasets in which individuals are described both by their own features and by their relations to other individuals, arise from various sources such as databases, both relational and object-oriented, knowledge bases, or software models, e.g., UML class diagrams. When processing such complex datasets, it is of prime importance for an analysis tool to hold as much as possible to the initial format so that the semantics is preserved and the interpretation of the final results eased. Therefore, several attempts have been made to introduce relations into the formal concept analysis field which otherwise generated a large number of knowledge discovery methods and tools. However, the proposed approaches invariably look at relations as an intra-concept construct, typically relating two parts of the concept description, and therefore can only lead to the discovery of coarse-grained patterns. As an approach towards the discovery of finer-grain relational concepts, w...

• Type: Journal
• Year: 2007
• Where: AMAI
• Authors: Marianne Huchard, Mohamed Rouane Hacene, Cyril Roume, Petko Valtchev
• Keywords: AMAI 2007, Artificial Intelligence, Relational Datasets, UML Class Diagrams, finer-grain Relational Concepts