On generating all maximal independent sets
Information Processing Letters
Attribute exploration with background knowledge
Theoretical Computer Science
Efficient mining of association rules using closed itemset lattices
Information Systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Theory of Relational Databases
Theory of Relational Databases
Formal concept analysis with background knowledge: attribute priorities
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Pseudo-models and propositional Horn inference
Discrete Applied Mathematics - Ordinal and symbolic data analysis (OSDA 2000)
Association rule mining: models and algorithms
Association rule mining: models and algorithms
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We present a method of imposing constraints in extracting formal concepts (equivalently, closed itemsets or fixpoints of Galois connections) from a binary relation. The constraints are represented by closure operators and their purpose is to mimic background knowledge a user may have of the data. The idea is to consider and extract only these itemsets that are compatible with the background knowledge. As a result, the method extracts less clusters, those that are interesting from the user point of view, in a shorter time. The method makes it also possible to extract minimal bases of attribute dependencies in the input data that are compatible with the background knowledge. We provide examples of several particular types of constraints including those that appeared in the literature in the past and present algorithms to compute the constrained formal concepts and attribute implications.