Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
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
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in 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 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
On the evaluation of the decision performance of an incomplete decision table
Data & Knowledge Engineering
Applying Rough Sets to Data Tables Containing Missing Values
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Applying Rough Sets to Information Tables Containing Probabilistic Values
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Rough Sets under Non-deterministic Information
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Lower and upper approximations in data tables containing possibilistic information
Transactions on rough sets VII
A new decision tree construction using the cloud transform and rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Applying rough sets to information tables containing possibilistic values
Transactions on computational science II
A fuzzy view on rough satisfiability
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Unit operations in approximation spaces
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Journal of Intelligent Information Systems
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
Generalized approximations defined by non-equivalence relations
Information Sciences: an International Journal
Covering based rough set approximations
Information Sciences: an International Journal
An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Data-Driven valued tolerance relation
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
Composite rough sets for dynamic data mining
Information Sciences: an International Journal
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This paper shows that attribute-value pair blocks, used for many years in rule induction, may be used as well for computing indiscernibility relations for completely specified decision tables. Much more importantly, for incompletely specified decision tables, i.e., for data with missing attribute values, the same idea of attribute-value pair blocks is a convenient tool to compute characteristic sets, a generalization of equivalence classes of the indiscernibility relation, and also characteristic relations, a generalization of the indiscernibility relation. For incompletely specified decision tables there are three different ways lower and upper approximations may be defined: singleton, subset and concept. Finally, it is shown that, for a given incomplete data set, the set of all characteristic relations for the set of all congruent decision tables is a lattice.