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
Knowledge Spaces
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
Adding background knowledge to formal concept analysis via attribute dependency formulas
Proceedings of the 2008 ACM symposium on Applied computing
Cluster Analysis
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 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
A new algebraic structure for formal concept analysis
Information Sciences: an International Journal
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Selecting important concepts using weights
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
A novel attribute reduction approach based on the object oriented concept lattice
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Knowledge discovery in data using formal concept analysis and random projections
International Journal of Applied Mathematics and Computer Science
Basic level of concepts in formal concept analysis
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
Extracting reducible knowledge from ANN with JBOS and FCANN approaches
Expert Systems with Applications: An International Journal
Closure-based constraints in formal concept analysis
Discrete Applied Mathematics
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Basic level in formal concept analysis: interesting concepts and psychological ramifications
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Rule acquisition and complexity reduction in formal decision contexts
International Journal of Approximate Reasoning
Granularity of attributes in formal concept analysis
Information Sciences: an International Journal
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This paper deals with background knowledge in knowledge extraction from binary data. A background knowledge represents an additional piece of information a user may have along with the input data. Such information can be considered as specifying the type of knowledge a user is looking for in the data. In particular, we emphasize the need for taking into account background knowledge in formal concept analysis. We present an approach to modeling background knowledge that represents user's priorities regarding attributes and their relative importance. Such priorities serve as a constraint--only those formal concepts that are compatible with user's priorities are considered relevant, extracted from data, and presented to the user. Our approach has two main practical features. First, the number of formal concepts presented to the user may get significantly reduced. As a result, the user is supplied with relevant formal concepts only and is not overloaded by a large number of possibly nonrelevant formal concepts. Second, different priorities lead to different pieces of knowledge extracted from data. This way, the input data may be repeatedly used in knowledge extraction for different purposes corresponding to different priorities. We concentrate on foundational aspects such as mathematical feasibility, reasoning with background knowledge, removing redundancy from background knowledge, and computational tractability, and present several illustrative examples. In addition, we discuss directions for future research.