Parameterizing the Semantics of Fuzzy Attribute Implications by Systems of Isotone Galois Connections

Download Now
Provided by: Cornell University
Topic: Data Management
Format: PDF
The authors study the semantics of fuzzy if-then rules called fuzzy attribute implications parameterized by systems of isotone Galois connections. The rules express dependencies between fuzzy attributes in object-attribute incidence data. The proposed parameterizations are general and include as special cases the parameterizations by linguistic hedges used in earlier approaches. They formalize the general parameterizations, propose bivalent and graded notions of semantic entailment of fuzzy attribute implications, show their characterization in terms of least models and complete axiomatization, and provide characterization of bases of fuzzy attribute implications derived from data.
Download Now

Find By Topic