Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Attribute exploration with background knowledge
Theoretical Computer Science
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Theory of Relational Databases
Theory of Relational Databases
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Formal concept analysis constrained by attribute-dependency formulas
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
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
Attribute exploration of many-valued context with SAT
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
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We present a way to add user's background knowledge to formal concept analysis. The type of background knowledge we deal with relates to relative importance of attributes in the input data. We introduce AD-formulas which represent this type of background knowledge. The background knowledge serves as a constraint. The main aim is to make extraction of clusters from the input data more focused by taking into account the background knowledge. Particularly, only clusters which are compatible with the background knowledge are extracted from data. As a result, the number of extracted clusters becomes smaller, leaving out non-interesting clusters. We present illustrative examples and results on entailment of background knowledge such as efficient testing of entailment and a complete systems of deduction rules.