Variable precision rough set model
Journal of Computer and System Sciences
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
On semantic issues connected with incomplete information databases
ACM Transactions on Database Systems (TODS)
On Databases with Incomplete Information
Journal of the ACM (JACM)
Information Sciences—Informatics and Computer Science: An International Journal
A relational model of data for large shared data banks
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
An Algorithm for Finding Equivalence Relations from Tables with Non-Deterministic Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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Non–deterministic Information Systems (NISs) are ad-vanced extensions of Deterministic Information Systems (DISs), and NISs are known well as systems handling information incompleteness in tables. This paper examines manipulations on equivalence relations in DISs and manipulations on possible equivalence relations in NISs. This paper also follows rough sets based rule generation in DISs, and proposes rough sets based hypothesis generation in NISs. A hypothesis in NISs is defined by a formula satisfying some kinds of constraint, and effective algorithms for generating hypotheses in NISs are presented. Possible equivalence relations play important roles in generating hypotheses. Some illustrative examples and real executions of algorithms are shown, too.