Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
International Journal of Approximate Reasoning
A parallel method for computing rough set approximations
Information Sciences: an International Journal
Neighborhood rough sets for dynamic data mining
International Journal of Intelligent Systems
Temporal Dynamics in Information Tables
Fundamenta Informaticae - From Physics to Computer Science: to Gianpiero Cattaneo for his 70th birthday
Attribute reduction for dynamic data sets
Applied Soft Computing
Multigranulation rough sets: From partition to covering
Information Sciences: an International Journal
Composite rough sets for dynamic data mining
Information Sciences: an International Journal
Information Sciences: an International Journal
An update logic for information systems
International Journal of Approximate Reasoning
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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In rough set theory, upper and lower approximations for a concept will change dynamically as the information system changes over time. How to update approximations based on the original information is an important task that can help improve the efficiency of knowledge discovery. This paper focuses on the approach of dynamically updating approximations when attribute values are coarsened or refined. The main contributions include: (1) defining coarsening and refining attribute values in information systems and introducing the properties and the principles of coarsening and refining attribute values; (2) analyzing the properties for dynamic maintenance in terms of upper and lower approximations with coarsening and refining attribute values; (3) proposing an incremental algorithm for updating the approximations of a concept as coarsening or refining attributes values; and finally (4) validating the efficiency of the proposed approach to handle the dynamic maintenance of the approximations for a given concept. © 2010 Wiley Periodicals, Inc.