Abstract
The concept of the rough set is a new mathematical approach to imprecision, vagueness and uncertainty in data analysis.
The starting point of the rough set philosophy is the assumption that with every object of interest we associate some information (data, knowledge). E.g., if objects are patients suffering form a certain disease, symptoms of the disease form information about patients. Objects are similar or indiscrenible, if they are characterized by the same information. The indiscernibility relation generated thus is the mathematical basis of the rough set theory.
Preview
Unable to display preview. Download preview PDF.
References
- [1]Grzymala-Busse J.W., (1991), Managing Uncertainty in Expert Systems. Kluwer Academic Publishers, Dordrecht, Boston, London.MATHCrossRefGoogle Scholar
- [2]Lin, T.Y., (ed.), (1994), The Third International Workshop on Rough Sets and Soft Computing Proceedings (RSSC’94), San Jose State University, San Jose, California, USA, November 10–12.Google Scholar
- [3]Pawlak Z., (1982), “Rough sets”. International Journal of Computer and Information Sciences, 11, 341–356.MathSciNetMATHCrossRefGoogle Scholar
- [4]Pawlak Z., (1991), Rough Sets — Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London.MATHGoogle Scholar
- [5]Pawlak Z., Grzymala-Busse J. W., Słowiński R., and Ziarko, W., (1995), “Rough sets”, Communication of the ACM, 38, 88–95.CrossRefGoogle Scholar
- [6]Słowiński, R., (ed.), (1992), Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht.Google Scholar
- [7]Ziarko, W., (ed.), (1993), Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD’93), Banff, Alberta, Canada, October 12–15, Springer-Verlag, Berlin.Google Scholar
Copyright information
© Kluwer Academic Publishers 1997