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Rough Sets in Knowledge Discovery

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.
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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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