Attribute exploration with background knowledge
Theoretical Computer Science
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
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
Formal concept analysis with constraints by closure operators
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Formal concept analysis constrained by attribute-dependency formulas
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Formal concept analysis as a framework for business intelligence technologies
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Applying the JBOS reduction method for relevant knowledge extraction
Expert Systems with Applications: An International Journal
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We present an approach that enables one to select a reasonable small number of possibly important formal concepts from the set of all formal concepts of a given input data. The problem to select a small number of concepts appears in applications of formal concept analysis when the number of all formal concepts of the input data is large. Namely, a user often asks for a list of "important concepts" in such case. In the present approach, attributes of the input data are assigned weights from which values of formal concepts are determined. Formal concepts with larger values are considered more important. The attribute weights are supposed to be set by the users. The approach is a continuation of our previous approaches that utilize background knowledge, i.e. additional knowledge of a user, to select parts of concept lattices. In addition to the approach, we present illustrative examples.