Abstract
Information systems with real value multivalued attributes are considered. It is also assumed that some of the values describing objects in the system are uncertain. A procedure for quantization of sets of real numbers is presented. It transforms the information system into a reduced system, where the sets of reals are represented by ranges. The method generates ranges directly from the source table. As a result a set of optimal decision rules is provided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pawlak Z (1991) Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Vol. 9
Hu X, Cercone DN, Han J (1993) An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases. Proc. of the Intl Workshop on Rough Sets and Knowledge Discovery RSKD ‘83: 79–94
Xiang A, Wong SKM, Cercone N (1993) Quantifying Uncertainty of Knowledge Discovered From Databases. Proc. of the Intl Workshop on Rough Sets and Knowledge Discovery RSKD ‘83: 51–60
Atkinson M, DeWitt D, Meier D, Bacilhon F, Dittrich K, Zdonik S. (1989) The Object Oriented Database System Manifesto. Proc. of 1st Intl Conf. on Deductive and Object-Oriented Databases
Abiteboul S, Hull R (1987) IFO: A Formal Semantic Database Model. ACM Trans. Database Syst., Vol. 12, No°3
Slowinski R, Stefanowski J (1989) Rough Classification in Incomplete Information Systems. Mathematical and Comput. Modelling, Vol. 12, No 10–11: 1347–1357
Slowinski R, Stefanowski J (1994) Handling Various Types of Uncertainty in The Rough Set Approach. Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD ‘83), W. Ziarko (ed. ), Springer-Verlag
Lenarcik A, Piasta Z (1994) Rough Classifiers. Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD ‘83), W. Ziarko (ed. ), Springer-Verlag
Skowron A, Nguyen S. (1995) Quantization of Real Value Attributes: Rough Set and Boolean Reasoning Approach. Second Annual Joint Conference on Information Sciences: Fuzzy Logic, Neural Computing, Pattern Recognition, Computer Vision, Evolutionary Computing, Information Theory, Computational Intelligence, Wrightsville Beach, North Carolina, USA, 28 September–1 October: 34–37
Kryszkiewicz M (1997) Rough Set Approach to Incomplete Information Systems. To appear in Information Sciences
Hadjimichael M, Wong SMK (1993) Fuzzy Representation in Rough Set Approximations. Proc. of the Intl Workshop on Rough Sets and Knowledge Discovery RSKD ‘83,: 371–381.
Chmielewski MR, Grzymala-Busse JW (1992) Global Discretization of Continuous Attributes as Preprocessing for Inductive Learning. Technical Report TR-92–7, Department of Computer Science, The University of Kansas, Lawrence, KS 66045
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kryszkiewicz, M., Rybinski, H. (2000). Reducing Information Systems with Uncertain Real Value Attributes. In: Pons, O., Vila, M.A., Kacprzyk, J. (eds) Knowledge Management in Fuzzy Databases. Studies in Fuzziness and Soft Computing, vol 39. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1865-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1865-9_20
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2467-4
Online ISBN: 978-3-7908-1865-9
eBook Packages: Springer Book Archive