Granularity of attributes in formal concept analysis

DOI10.1016/j.ins.2013.10.021zbMath1328.68216OpenAlexW2018012025WikidataQ60256189 ScholiaQ60256189MaRDI QIDQ903595

Jan Konecny, Radim Bělohlávek, Bernard De Baets

Publication date: 14 January 2016

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2013.10.021

zbMATH Keywords

formal concept analysisconcept latticebinary dataalgorithms for data managementinteractive data exploration

Mathematics Subject Classification ID

Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37) Complete lattices, completions (06B23)

Related Items (16)

Formal Concept Analysis and Extensions for Complex Data Analytics ⋮ A comprehensive survey on formal concept analysis, its research trends and applications ⋮ Granular matrix method of attribute reduction in formal contexts ⋮ Fuzzy concept lattice reduction using Shannon entropy and Huffman coding ⋮ Attribute granulation in fuzzy formal contexts based on \(L\)-fuzzy concepts ⋮ On stability of fuzzy formal concepts over randomized one-sided formal context ⋮ Residuated lattices of block relations: size reduction of concept lattices ⋮ Formal Concept Analysis with Constraints by EM Operators ⋮ Constraint heterogeneous concept lattices and concept lattices with heterogeneous hedges ⋮ Vague concept lattice reduction using granular computing and vague entropy ⋮ Concept learning via granular computing: a cognitive viewpoint ⋮ On the size of \(\exists \)-generalized concept lattices ⋮ Attribute reduction in generalized one-sided formal contexts ⋮ On Fuzzy Generalizations of Concept Lattices ⋮ A constructing approach to multi-granularity object-induced three-way concept lattices ⋮ Randomized Fuzzy Formal Contexts and Relevance of One-Sided Concepts

Uses Software

• UCI-ml

Cites Work

• Discovery of optimal factors in binary data via a novel method of matrix decomposition
• Isotone fuzzy Galois connections with hedges
• Attribute exploration with background knowledge
• Comparing performance of algorithms for generating concept lattices
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