Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Uncertainly measures of rough set prediction
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
Formal Concept Analysis in Relational Database and Rough Relational Database
Fundamenta Informaticae
An information entropy-based approach to outlier detection in rough sets
Expert Systems with Applications: An International Journal
A rough set approach to feature selection based on relative decision entropy
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Advances in fuzzy rough set theory for temporal databases
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Formal Concept Analysis in Relational Database and Rough Relational Database
Fundamenta Informaticae
Relational Operations and Uncertainty Measure in Rough Relational Database
Fundamenta Informaticae
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Beaubouef, Petry and Buckles proposed the generalized rough set database analysis (GRSDA) to discuss rough relational databases. Given any rough relational database (U, A) and an attribute a ∈ A, as in rough set theory, a definition of the lower and upper approximations based on φa is given. The entropy and conditional entropy of similarity relations in a rough relational database are defined. The examples show that the entropy of a similarity relation does not decrease as the similarity relation is refined. It will be proved that given any two similarity relations φ and ψ, defined by a set C of conditional attributes and a decision attribute d, respectively, if d similarly depends on C in a rough relational database then the conditional entropy of φ with respect to ψ is equal to the entropy of φ.