Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
On generalizing rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Mining Numerical Data--A Rough Set Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Interpreting Low and High Order Rules: A Granular Computing Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Credible rules in incomplete decision system based on descriptors
Knowledge-Based Systems
Rules and Apriori Algorithm in Non-deterministic Information Systems
Transactions on Rough Sets IX
Approximation Space and LEM2-like Algorithms for Computing Local Coverings
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Dynamic reduction based on rough sets in incomplete decision systems
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Descriptors and templates in relational information systems
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A note on definability and approximations
Transactions on rough sets VII
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Applying rough sets to information tables containing possibilistic values
Transactions on computational science II
Local and global approximations for incomplete data
Transactions on rough sets VIII
Research on the model of rough set over dual-universes
Knowledge-Based Systems
Unit operations in approximation spaces
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Valued dominance-based rough set approach to incomplete information system
Transactions on computational science XIII
Mining incomplete data: a rough set approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A rough set approach to data with missing attribute values
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Mining numerical data – a rough set approach
Transactions on Rough Sets XI
Definability and other properties of approximations for generalized indiscernibility relations
Transactions on Rough Sets XI
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Generalized approximations defined by non-equivalence relations
Information Sciences: an International Journal
Approximation Space and LEM2-like Algorithms for Computing Local Coverings
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Extended tolerance relation to define a new rough set model in incomplete information systems
Advances in Fuzzy Systems
Rough set based pose invariant face recognition with mug shot images
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In incomplete data missing attribute values may be universally interpreted in several ways. Four approaches to missing attribute values are discussed in this paper: lost values, ”do not care” conditions, restricted ”do not care” conditions, and attribute-concept values. Rough set ideas, such as attribute-value pair blocks, characteristic sets, characteristic relations and generalization of lower and upper approximations are used in these four approaches. A generalized rough set methodology, achieved in the process, may be used for other applications as well. Additionally, this generalized methodology is compared with other extensions of rough set concepts.