Abstract
The methodology we propose and develop in this book is founded on the concept of rough sets. In many AI applications one faces the problem of representing and processing incomplete, imprecise, and approximate data. Many of these applications require the use of approximate reasoning techniques. Before we introduce rough sets formally, let us begin with an intuitive example where representation of approximate data and reasoning with it is an essential component in the modeling process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this chapter
Cite this chapter
Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A. (2006). Rough Sets. In: Knowledge Representation Techniques. Studies in Fuzziness and Soft Computing, vol 202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33519-6_3
Download citation
DOI: https://doi.org/10.1007/3-540-33519-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33518-4
Online ISBN: 978-3-540-33519-1
eBook Packages: EngineeringEngineering (R0)