Incremental learning of rules and Meta-rules
Proceedings of the seventh international conference (1990) on Machine learning
Inductive learning, numerical criteria and combinatorial optimization, some results
Proceedings of the conference on Data analysis, learning symbolic and numeric knowledge
A relation between the theory of formal concepts and multiway clustering
Pattern Recognition Letters
An introduction to symbolic data analysis and the SODAS software
Intelligent Data Analysis
Inductive learning from numerical and symbolic data: An integrated framework
Intelligent Data Analysis
Discrete Applied Mathematics
Cluster structures and collections of Galois closed entity subsets
Discrete Applied Mathematics
Real formal concept analysis based on grey-rough set theory
Knowledge-Based Systems
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We study assertion objects that constitute a particular class of symbolic objects. Symbolic objects constitute a data analysis driven formalism, which can be compared to propositional calculus, but which is oriented toward the duality intension (characteristic properties) versus extension (set of all individuals verifying a given set of properties). The set of assertion objects is endowed with a partial order and a quasi-order. We focus on the property of completeness, which precisely expresses the duality intension-extension. The order structure of complete assertion objects is studied, using notions of lattice theory and Galois connection, and extending R. Wille's work (1982) to multiple-valued data. Two results are then obtained for particular cases.