Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rules in incomplete information systems
Information Sciences: an International Journal
Incomplete Information: Structure, Inference, Complexity
Incomplete Information: Structure, Inference, Complexity
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Approximation Space and LEM2-like Algorithms for Computing Local Coverings
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Incomplete data and generalization of indiscernibility relation, definability, and approximations
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Another approach to soft rough sets
Knowledge-Based Systems
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In this paper we analyze the basic concepts of rough set theory, lower and upper approximations, defined in an approximation space (U, L), where U is a nonempty and finite set and L is a fixed family of subsets of U. Some definitions of such lower and upper approximations are well known, some are presented in this paper for the first time. Our new definitions better accommodate applications to mining incomplete data, i.e., data with missing attribute values. An illustrative example is also presented in this paper.