Rough sets, fuzzy sets, and knowledge discovery : proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD '93), Banff, Alberta, Canada, 12-15 October, 1993

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. Front Matter....Pages i-x An Overview of Knowledge Discovery in Databases: Recent Progress and Challenges....Pages 1-10 Rough Sets and Knowledge Discovery: An Overview....Pages 11-15 Search for Concepts and Dependencies in Databases....Pages 16-23 Rough Sets and Concept Lattices....Pages 24-31 Human-Computer Interfaces: DBLEARN and SystemX....Pages 32-43 A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN....Pages 44-51 Knowledge Recognition, Rough Sets, and Formal Concept Lattices....Pages 52-61 Quantifying Uncertainty of Knowledge Discovered From Databases....Pages 63-73 Temporal Rules Discovery using Datalogic/R+ with Stock Market Data....Pages 74-81 A System Architecture for Database Mining Applications....Pages 82-89 An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases....Pages 90-99 A Rough Set Model for Relational Databases....Pages 100-107 Data Filtration: A Rough Set Approach....Pages 108-118 Automated Discovery of Empirical Laws in a Science Laboratory....Pages 119-129 Hard and Soft Sets....Pages 130-135 Rough Set Analysis of Multi-Attribute Decision Problems....Pages 136-142 Rough Set Semantics for Non-classical Logics....Pages 143-148 A Note on Categories of Information Systems....Pages 149-156 On Rough Sets in Topological Boolean Algebras....Pages 157-160 Approximation of Relations....Pages 161-166 Variable Precision Rough Sets with Asymmetric Bounds....Pages 167-177 Uncertain Reasoning with Interval-Set Algebra....Pages 178-185 On a Logic of Information for Reasoning about Knowledge....Pages 186-195 Rough Consequence and Rough Algebra....Pages 196-207 Formal Description of Rough Sets....Pages 208-216 Rough Sets: A Special Case of Interval Structures....Pages 217-226 A Pure Logic-algebraic Analysis of Rough Top and Rough Bottom Equalities....Pages 227-236 A Novel Approach to the Minimal Cover Problem....Pages 237-241 Algebraic Structures of Rough Sets....Pages 242-247 Rough Concept Analysis....Pages 248-255 Rough Approximate Operators: Axiomatic Rough Set Theory....Pages 256-260 Finding Reducts in Composed Information Systems....Pages 261-273 PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory....Pages 274-281 Comparison of Machine Learning and Knowledge Acquisition Methods of Rule Induction Based on Rough Sets....Pages 282-289 AQ, Rough Sets, and Matroid Theory....Pages 290-297 Rough Classifiers....Pages 298-316 A General Two-Stage Approach to Inducing Rules from Examples....Pages 317-325 An Incremental Learning Algorithm for Constructing Decision Rules....Pages 326-334 Decision Trees for Decision Tables....Pages 335-342 Fuzzy Reasoning and Rough Sets....Pages 343-348 Fuzzy Representations in Rough Set Approximations....Pages 349-356 Trusting an Information Agent....Pages 357-365 Handling Various Types of Uncertainty in the Rough Set Approach....Pages 366-376 Intelligent Image Filtering Using Rough Sets....Pages 377-386 Multilayer Knowledge Base System for Speaker-Independent Recognition of Isolated Words....Pages 387-394 Image Segmentation Based on the Indiscernibility Relation....Pages 395-402 Accurate Edge Detection Using Rough Sets....Pages 403-411 Rough Classification of Pneumonia Patients using a Clinical Database....Pages 412-419 Rough Sets Approach to Analysis of Data of Diagnostic Peritoneal Lavage Applied for Multiple Injuries Patients....Pages 420-425 Neural Networks and Rough Sets — Comparison and Combination for Classification of Histological Pictures....Pages 426-433 Towards a Parallel Rough Sets Computer....Pages 434-443 Learning Conceptual Design Rules: A Rough Sets Approach....Pages 444-449 Intelligent Control System Implementation to the Pipe Organ Instrument....Pages 450-457 An Implementation of Decomposition Algorithm and its Application in Information Systems Analysis and Logic Synthesis....Pages 458-465 ESEP: An Expert System for Environmental Protection....Pages 466-473 Back Matter....Pages 475-478 This volume contains papers from the International Workshop on Rough Sets and Knowledge Discovery, held in Banff, Alberta, Canada, from 12 - 15 October 1993. It includes application reports and theory-oriented papers by leading professionals in both of these important areas of research. There are comprehensive sections on the following topics: knowledge discovery methodologies and applications; fundamental research in the theory of rough sets and its extensions; machine learning applications of the rough sets approach (including presentation of some new learning methods); reasoning from uncertain information using a combination of fuzzy and rough sets methods; and applications of the methodologies in areas such as market analysis, medical diagnosis, and image processing. The resulting volume will be of interest to researchers and developers of systems for knowledge acquisition, decision support and machine discovery