A rough set approach to attribute generalization in data mining
Information Sciences: an International Journal
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rough set approach to incomplete information systems
Information Sciences: 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
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
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
RRIA: a rough set and rule tree based incremental knowledge acquisition algorithm
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
International Journal of Approximate Reasoning
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Axiomatic systems for rough sets and fuzzy rough sets
International Journal of Approximate Reasoning
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Set-valued ordered information systems
Information Sciences: an International Journal
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
International Journal of Intelligent Systems
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
Neighborhood rough sets based matrix approach for calculation of the approximations
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Multi knowledge based rough approximations and applications
Knowledge-Based Systems
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
Set-valued information systems
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Neighborhood rough sets for dynamic data mining
International Journal of Intelligent Systems
The Axiomatization of the Rough Set Upper Approximation Operations
Fundamenta Informaticae
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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As a soft computing tool, rough set theory has become a popular mathematical framework for pattern recognition, data mining and knowledge discovery. It can only deal with attributes of a specific type in the information system by using a specific binary relation. However, there may be attributes of multiple different types in information systems in real-life applications. Such information systems are called as composite information systems in this paper. A composite relation is proposed to process attributes of multiple different types simultaneously in composite information systems. Then, an extended rough set model, called as composite rough sets, is presented. We also redefine lower and upper approximations, positive, boundary and negative regions in composite rough sets. Through introducing the concepts of the relation matrix, the decision matrix and the basic matrix, we propose matrix-based methods for computing the approximations, positive, boundary and negative regions in composite information systems, which is crucial for feature selection and knowledge discovery. Moreover, combined with the incremental learning technique, a novel matrix-based method for fast updating approximations is proposed in dynamic composite information systems. Extensive experiments on different data sets from UCI and user-defined data sets show that the proposed incremental method can process large data sets efficiently.