Methodology and applications
著者
書誌事項
Methodology and applications
（Studies in fuzziness and soft computing, v.18 . Rough sets in knowledge discovery / Lech Polkowski,
Physica-Verlag, c1998
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注記
Includes bibliographical references
内容説明・目次
内容説明
The ideas and techniques worked out in Rough Set Theory allow for knowledge reduction and to finding near - to - functional dependencies in data. This fact determines the importance of these techniques for the rapidly growing field of knowledge discovery. Volume 1 and 2 will bring together articles covering the present state of the methods developed in this field of research. Among the topics covered we may mention: rough mereology and rough mereological approach to knowledge discovery in distributed systems; discretization and quantization of attributes; morphological aspects of rough set theory; analysis of default rules in the framework of rough set theory.
目次
- Z. Pawlak: Foreword.- Introduction: L. Polkowski, A. Skowron: Introducing the Book
- Z. Pawlak: Rough Set Elements
- L. Polkowski, A. Skowron: Rough Sets: A Perspective.- Foundations: G. Cattaneo: Abstract Approximation Spaces for Rough Theories
- S. Demri, E. Orlowska: Complementarity Relations: Reduction of Decision Rules and Informational Representability
- T.Y. Lin: Granular Computing on Binary Relations I. Data Mining and Neighborhood Systems
- T.Y. Lin: Granular Computing II. Rough Set Representations and Belief Functions
- S. Miyamaoto: Fuzzy Multisets and a Rough Approximation by Multiset-Valued Function
- M. Moshkov: On Time Complexity of Decision Trees
- A. Nakamura: Graded Modalities in Rough Logic
- P. Pagliani: A Practical Introduction to the Model-Relational Approach to Approximation Spaces
- E. SanJuan, L. Iturrioz: Duality and Information Representability of some Information Algebras
- J. Stepaniuk: Rough Relations and Logics
- A. Wasilewska, L. Vigneron: Rough Algebras and Automated Deduction
- S.K.M. Wong: A Rough-Set Model for Reasoning about Knowledge
- Y.Y. Yao: Generalized Rough Set Models.- Methods and Applications: J.G. Bazan: A Comparison of Dynamic and Non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables
- J.W. Grzymala-Busse: Applications of the Rule Induction Systems LERS
- A. Ohrn, J. Komorowski, A. Skowron, P. Synak: The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets - The ROSETTA System
- W. Kowalczyk: Rough Data Modelling: a New Technique for Analyzing Data
- M. Kryszkiewicz: Properties of Incomplete Information Systems in the Framework of Rough Sets
- H. Son Nguyen, S. Hoa Nguyen: Discretization Methods in Data Mining
- Z. Piasta, A. Lenarcik: Learning Rough Classifiers from Large Databases with Missing Values
- J. Stefanowski: On Rough Set Based Approaches to Induction of Decision Rules
- R. Susmaga: Experiments in Incremental Computation of Reducts
- W. Ziarko: Rough Sets as a Methodology for Data Mining.
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