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
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A Feature Selection Algorithm Based on Discernibility Matrix
Computational Intelligence and Security
Soft Minimum-Enclosing-Ball Based Robust Fuzzy Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Reduct generation of microarray dataset using rough set and graph theory for unsupervised learning
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Management of Bus Driver Duties Using Data Mining
International Journal of Applied Metaheuristic Computing
| Hi-index | 0.00 |
Rough sets theory was proposed by Pawlak in the early 1980ï戮聮s 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.