Attribute exploration with background knowledge
Theoretical Computer Science
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
On the Treatment of Incomplete Knowledge in Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
An FCA Perspective on n-Distributivity
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Base points, non-unit implications, and convex geometries
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
About the family of closure systems preserving non-unit implications in the guigues-duquenne base
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
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We propose a variation of the attribute exploration algorithm. Instead of implications with pseudo-intents as premises our approach uses implications with proper premises. It is known that the set of implications with proper premises is complete, but in general it is not minimal in size. This variation will allow us to calculate all implications of a formal context with premise size at most n, for some fixed $n\in\mathbb N$. This is of interest if the attribute set is large and the user requests valid implications with small premises. Other applications can be seen for formal contexts where the maximal premise size of an implication with proper premise is known, for example multivalued contexts scaled by multiordinal scales only.