Showing papers by "Zdzisław Pawlak published in 1998"
TL;DR: The lower and upper approximation of a set, the basic operations of the theory, are intuitively explained and formally defined.
Abstract: This paper gives basic ideas of rough set theory a new approach to data analysis The lower and upper approximation of a set, the basic operations of the theory, are intuitively explained and formally defined Some applications of rough set theory are briefly outlined and some future problems are outlined
754 citations
04 May 1998
TL;DR: Basic concepts of rough set theory are defined and their granular structure pointed out and the consequences of granularity of knowledge for reasoning about imprecise concepts are discussed.
Abstract: Granularity of knowledge has attracted attention of many researchers. This paper concerns this issue from the rough set perspective. Granularity is inherently connected with the foundation of rough set theory. The concept of the rough set hinges on classification of objects of interest into similarity classes, which form elementary building blocks (atoms, granules) of knowledge. These granules are employed to define basic concepts of the theory. In the paper basic concepts of rough set theory are defined and their granular structure pointed out. Next the consequences of granularity of knowledge for reasoning about imprecise concepts are discussed.
233 citations
TL;DR: A novel approach to conflict analysis, based on rough set theory, is outlined and basic concepts of this approach are defined and analyzed.
231 citations
[...]
01 Jan 1998
TL;DR: This chapter discusses the development of rough sets for knowledge discovery in the context of fuzzy systems and their applications in the rapidly changing environment.
Abstract: W: L. Polkowski and A. Skowron, editors, Rough Sets in Knowledge Discovery 1. Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing, pages 10-30. Springer-Verlag, Heidelberg, Germany, 1998
55 citations
TL;DR: It is shown that decision rules can be interpreted as a generalization of the modus ponens inference rule, however there is an essential difference between these two concepts.
Abstract: The paper contains some considerations concerning the relationship between decision rules and inference rules from the rough set theory perspective. It is shown that decision rules can be interpreted as a generalization of the modus ponens inference rule, however there is an essential difference between these two concepts. Decision rules in the rough set approach are used to describe dependencies in data, whereas modus ponens is used in general to derive conclusions from premises.
46 citations
TL;DR: Rough sets is discussed relative to these objectives, as is current research to address its limitations and difficulties in application.
45 citations
[...]
01 Jan 1998
TL;DR: This paper presents a meta-analyses of the determinants of uncertainty in knowledge-Based Systems and their applications to knowledge-based pedagogical practices.
Abstract: w: Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 1998), vol. 2, La Sorbone, Paris, 1998, pages 1162-1166, 1998
37 citations
01 Jan 1998
TL;DR: The Fuzzy-Neuro Systems - Computational Intelligence, Muenchen, Germany, 18-20 March, 1998, pages 1-9.
Abstract: W: W. Bauer, editor, Proceedings of the Fuzzy-Neuro Systems - Computational Intelligence, Muenchen, Germany, 18-20 March, 1998, pages 1-9. 1998
8 citations
01 Jan 1998
TL;DR: In the paper basic concepts of rough set theory will be presented and discussed and its applications can be found in the enclosed references.
Abstract: Rough set theory is a new mathematical approach to data analysis. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition. It seems of particular importance to decision support systems and data mining. The theory has found many applications. In the paper basic concepts of rough set theory will be presented and discussed. More about rough set theory and its applications can be found in the enclosed references.
1 citations