Showing papers by "Zdzisław Pawlak published in 1999"
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01 Aug 1999-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: This article contains a new concept of approximate analysis of data, based on the idea of a “rough” set, which is shown as an example to medical data analysis.
Abstract: This article contains a new concept of approximate analysis of data, based on the idea of a “rough” set. The notion of approximate (rough) description of a set is introduced and investigated. The application to medical data analysis is shown as an example.
135 citations
09 Nov 1999
TL;DR: This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective and concludes that Bayes' inference rule should be considered as a guide to decision rules.
Abstract: This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective.
57 citations
01 Jan 1999
TL;DR: Rough Fuzzy Hybridization, A New Trend in and Decision Making, A new trend in and decision Making, pages 99-109.
Abstract: W: S.K. Pal and A. Skowron, editors, Rough Fuzzy Hybridization, A New Trend in and Decision Making, pages 99-109. Springer-Verlag, Singapore, 1999
35 citations
01 Jul 1999
TL;DR: In the paper basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined and some may be of extreme importance in the future are outlined.
Abstract: Application of intelligent methods in industry has become a very challenging issue nowadays and will be of extreme importance in the future. Intelligent methods include fuzzy sets, neural networks, genetic algorithms and other techniques known as soft computing. No doubt, rough set theory can also contribute to this domain. In the paper basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined.
24 citations
26 Apr 1999
TL;DR: This paper aims to provide a history of data mining in China and its applications in the context of knowledge discovery and data mining from 1989 to 1999.
Abstract: W: N. Zhong and L. Zhou, editors, Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 1999), Beijing, China, April, 1999, volume 1574, pages 3-11, Heidelberg, Germany, January 1999. Springer-Verlag
14 citations
01 Jan 1999
TL;DR: With every decision rule two conditional probabilities are associated called the certatinty and coverage factors, respectively, it is shown that these coefficients satisfy the Bayes’ theorem.
Abstract: The paper analyzes some properties of decision rules in the framework of rough set theory and knowledge discovery systems. With every decision rule two conditional probabilities are associated called the certatinty and coverage factors, respectively. It is shown that these coefficients satisfy the Bayes’ theorem. This relationship can be used as a new approach to Bayesian reasoning, without referring to prior and posterior probabilities, inherently associated with classical Bayesian inference. Decision rules are implications and the relationship between implications and Bayes’ theorem first was revealed by Lukasiewicz in connection with his multivaled logic.
7 citations