Abstract
In a rough set approach to knowledge discovery problems, a set of rules is generated basing on training data using a notion of reduct. Because a problem of finding short reducts is NP-hard, we have to use several approximation techniques. A covering approach to the problem of generating rules based on information system is presented in this article. A new, efficient algorithm for finding local reducts for each object in data table is described, as well as its parallelization and some optimization notes. A problem of working with tolerances in our algorithm is discussed. Some experimental results generated on large data tables (concerned with real applications) are presented.
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.
References
Bazan J., Skowron A., Synak P., 1994. Dynamic reducts as a tool for extracting laws from decision tables, Proc. of the Symp. on Methodologies for Intelligent Systems, Charlotte, NC, October 16–19, 1994, Lecture Notes in Artificial Intelligence 869, Springer-Verlag, Berlin 1994, 346–355, also in: ICS Research Report 43/94, Warsaw University of Technology.
Bazan J., 1998. A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In: L. Polkowski, A. Skowron (eds.). Rough Sets in Knowledge Discovery. Physica Verlag, 1998.
Goldberg D.E., 1989. GA in Search, Optimisation, and Machine Learning. Addison-Wesley.
Nguyen S. H., Skowron A., Synak P., Wróblewski J., 1997. Knowledge Discovery in Databases: Rough Set Approach. Proc. of The Seventh International Fuzzy Systems AssociationWorld Congress, vol. II, pp. 204–209, IFSA97, Prague, Czech Republic.
Nguyen H. S., Nguyen S. H., 1998. Discretization Methods in Data Mining. In: L. Polkowski, A. Skowron (eds.). Rough Sets in Knowledge Discovery. Physica Verlag, 1998.
Øhrn A., Komorowski J., 1997. Rosetta-A rough set toolkit for analysis of data. Proc. of Third International Join Conference on Information Sciences (JCIS97), Durham, NC, USA, March 1–5, 3 (1997), pp. 403–407.
Pawlak Z., 1991. Rough sets: Theoretical aspects of reasoning about data. Kluwer: Dordrecht 1991.
Skowron A., Rauszer C., 1992. The Discernibility Matrices and Functions in Information Systems. In: R. Slowiński (ed.): Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer: Dordrecht 1992, pp. 331–362.
Wróblewski J., 1995. Finding minimal reducts using genetic algorithms. Proc. of the Second Annual Join Conference on Information Sciences, pp.186–189, September 28–October 1, 1995, Wrightsville Beach, NC. Also in: ICS Research report 16/95, Warsaw University of Technology.
Wróblewski J., 1996. Theoretical Foundations of Order-Based Genetic Algorithms. Fundamenta Informaticae, vol. 28(3, 4), pp. 423–430. IOS Press, 1996.
Wróblewski J., 1998. Genetic algorithms in decomposition and classification problem. In: L. Polkowski, A. Skowron (eds.). Rough Sets in Knowledge Discovery. Physica Verlag, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wróblewski, J. (1998). Covering with Reducts - A Fast Algorithm for Rule Generation. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_55
Download citation
DOI: https://doi.org/10.1007/3-540-69115-4_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64655-6
Online ISBN: 978-3-540-69115-0
eBook Packages: Springer Book Archive