Discovering knowledge from fuzzy concept lattice
Data mining and computational intelligence
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fuzzy Relational Systems: Foundations and Principles
Fuzzy Relational Systems: Foundations and Principles
A multi-level conceptual data reduction approach based on the Lukasiewicz implication
Information Sciences: an International Journal - Special issue: Information technology
Crisply generated fuzzy concepts
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Information Sciences: an International Journal
Fuzzy Concept Lattices Determined by (θ,σ)-Fuzzy Rough Approximation Operators
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Attribute reduction in fuzzy concept lattices based on the T implication
Knowledge-Based Systems
Factorization of fuzzy concept lattices with hedges by modification of input data
Annals of Mathematics and Artificial Intelligence
The Construction of Fuzzy Concept Lattices Based on (&thgr;, σ)-Fuzzy Rough Approximation Operators
Fundamenta Informaticae - Knowledge Technology
Hi-index | 0.00 |
We focus on two approaches to formal concept analysis (FCA) of data with fuzzy attributes recently proposed in the literature, namely, on the approach via hedges and the approach via thresholds. Both of the approaches present parameterized ways to FCA of data with fuzzy attributes. Our paper shows basic relationships between the two of the approaches. Furthermore, we show that the approaches can be combined in a natural way, i.e. we present an approach in which one deals with both thresholds and hedges. We argue that while the approach via thresholds is intuitively appealing, it can be considered a special case of the approach via hedges. An important role in this analysis is played by so-called shifts of fuzzy attributes which appeared earlier in the study of factorization of fuzzy concept lattices. In addition to fuzzy concept lattices, we consider the idea of thresholds for the treatment of attribute implications from tables with fuzzy attributes and prove basic results concerning validity and non-redundant bases.