Optimizations of rough set model
Fundamenta Informaticae
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Logical and algebraic techniques for rough set data analysis
Rough set methods and applications
Beyond modalities: sufficiency and mixed algebras
Relational methods for computer science applications
Rough approximation quality revisited
Artificial Intelligence
Incomplete Information: Rough Set Analysis
Incomplete Information: Rough Set Analysis
Approximation Spaces in Extensions of Rough Set Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Brouwer-Zadeh posets and three-valued Ł ukasiewicz posets
Fuzzy Sets and Systems
Gauges, pregauges and completions: some theoretical aspects of near and rough set approaches to data
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Algebraic structures for rough sets
Transactions on Rough Sets II
Transactions on Rough Sets XVI
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In the context of generalized rough sets, it is possible to introduce in an Information System two different rough approximations. These are induced, respectively, by a Similarity and a Preclusivity relation ([3,4]). It is possible to show that the last one is always better than the first one. Here, we present a quantitative analysis of the relative performances of the two different approximations. The most important conclusion is that preclusive and similar approximation consistently differ when there is a low quality of approximation.