Variable precision rough set model
Journal of Computer and System Sciences
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
New Techniques for Data Reduction in a Database System for Knowledge Discovery Applications
Journal of Intelligent Information Systems
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Database System Implementation
Database System Implementation
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Rule Discovery from Databases with Decision Matrices
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Data mining, rough sets and granular computing
Data mining, rough sets and granular computing
GRS: a generalized rough sets model
Data mining, rough sets and granular computing
International Journal of Hybrid Intelligent Systems
Computers & Mathematics with Applications
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
High Frequent Value Reduct in Very Large Databases
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Modalities, Relations, and Learning
RelMiCS '09/AKA '09 Proceedings of the 11th International Conference on Relational Methods in Computer Science and 6th International Conference on Applications of Kleene Algebra: Relations and Kleene Algebra in Computer Science
Rule evaluations, attributes, and rough sets: extension and a case study
Transactions on rough sets VI
Hybrid rough sets-population based system
Transactions on rough sets VII
Hybrid rough sets intelligent system architecture for survival analysis
Transactions on rough sets VII
Hybrid mammogram classification using rough set and fuzzy classifier
Journal of Biomedical Imaging
Generate (F, ε)-dynamic reduct using cascading hashes
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Pairwise cores in information systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
The graph-theoretical properties of partitions and information entropy
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A rough set based model to rank the importance of association rules
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Introducing a rule importance measure
Transactions on Rough Sets V
Advances in fuzzy rough set theory for temporal databases
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
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Rough sets theory was proposed by Pawlak in the early 1980s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.