Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Data mining in incomplete information systems from rough set perspective
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Problem of Incomplete Information in Relational Databases
Problem of Incomplete Information in Relational Databases
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
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
On decomposition for incomplete data
Fundamenta Informaticae
Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences: an International Journal
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Rough-set-based approaches to data containing incomplete information: possibility-based cases
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
About the processing of possibilistic queries involving a difference operation
Fuzzy Sets and Systems
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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
Rough sets handling missing values probabilistically interpreted
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Checking whether or not rough-set-based methods to incomplete data satisfy a correctness criterion
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
Set-valued information systems
Information Sciences: an International Journal
Stable rule extraction and decision making in rough non-deterministic information analysis
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Generalized fuzzy rough description logics
Information Sciences: an International Journal
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Rough sets are applied to information tables containing imprecisevalues that are expressed in a normal possibility distribution. Amethod of weighted equivalence classes is proposed, where each equivalenceclass is accompanied by a possibilistic degree to which it is an actualone. By using a family of weighted equivalence classes, we derive lowerand upper approximations. The lower and upper approximations coincidewith ones obtained from methods of possible worlds. Therefore, themethod of weighted equivalence classes is justified. When this method isapplied to missing values interpreted possibilistically, it creates the samerelation for indiscernibility as the method of Kryszkiewicz that gave anassumption for indiscernibility of missing values. Using weighted equivalenceclasses correctly derives a lower approximation from the viewpointof possible worlds, although using a class of objects that is not an equivalenceclass does not always derive a lower approximation.