Overview

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.

Full Product Details

Author:   Lech Polkowski ,  Andrzej Skowron
Publisher:   Physica-Verlag GmbH & Co
Imprint:   Physica-Verlag GmbH & Co
Volume:   v. 18
Weight:   1.050kg
ISBN:  

9783790811193

ISBN 10:   379081119
Pages:   576
Publication Date:   July 1998
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

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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