Overview
- Editors:
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Wojciech P. Ziarko
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Department of Computer Science, University of Regina, Regina, Canada
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Table of contents (55 papers)
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- Gregory Piatetsky-Shapiro
Pages 1-10
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- Rokia Missaoui, Robert Godin
Pages 16-23
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- Nick Cercone, Paul McFetridge, Jiawei Han, Fred Popowich
Pages 32-43
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- David Fudger, Howard J. Hamilton
Pages 44-51
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- Walter A. Sedelow Jr., Sally Yeates Sedelow
Pages 52-61
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- Y. Xiang, S. K. M. Wong, N. Cercone
Pages 63-73
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- Robert Golan, Donald Edwards
Pages 74-81
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- Vijay V. Raghavan, Hayri Sever, Jitender S. Deogun
Pages 82-89
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- Xiaohua Hu, Nick Cercone, Jiawei Han
Pages 90-99
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- Theresa Beaubouef, Frederick E. Petry
Pages 100-107
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- J. A. Pomykala, E. de Haas
Pages 149-156
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About this book
The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification.
Editors and Affiliations
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Department of Computer Science, University of Regina, Regina, Canada
Wojciech P. Ziarko
