@BOOK { Rsds4365,
title = {Transactions on Rough Sets I},
series = = {Lecture Notes in Computer Science},
volume = 3100,
publisher = {Springer-Verlag},
address = {Heidelberg - Berlin, Germany},
year = 2004,
editor = {Peters, James F. and Skowron, Andrzej and Grzymala-Busse, Jerzy W. and Kostek, Bozena and Swiniarski, Roman W. and Szczuka, Marcin},
url = {http://www.springerlink.com/content/v9a60lxqrngh/?p=49175d4b821140269a3edf6404a8a53f&pi=2052},
issn = {0302-9743},
isbn = {3-540-22374-6},
abstract = {We would like to present, with great pleasure, the first volume of a new journal, Transactions on Rough Sets. This journal, part of the new journal subline in the Springer-Verlag series Lecture Notes in Computer Science, is devoted to the entire spectrum of rough set related issues, starting from logical and mathematical foundations of rough sets, through all aspects of rough set theory and its applications, data mining, knowledge discovery and intelligent information rocessing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets, theory of evidence, etc. The first, pioneering papers on rough sets, written by the originator of the idea, Professor Zdzislaw Pawlak, were published in the early 1980s. We are proud to dedicate this volume to our mentor, Professor Zdzislaw Pawlak, who kindly enriched this volume with his contribution on philosophical, logical, and mathematical foundations of rough set theory. In his paper Professor Pawlak shows all over again the underlying ideas of rough set theory as well as its relations with Bayes' theorem, conflict analysis, flow graphs, decision networks, and decision rules. After an overview and introductory article written by Professor Pawlak, the ten following papers represent and focus on rough set theory-related areas. Some papers provide an extension of rough set theory towards analysis of very large data sets, real data tables, data sets with missing values and rough non-deterministic information. Other theory-based papers deal with variable precision fuzzy-rough sets, consistency measure conflict profiles, and layered learning for concept synthesis. In addition, a paper on generalization of rough sets and rule extraction provides two different interpretations of rough sets. The last paper of this group addresses a partition model of granular computing. Other topics with a more application-orientated view are covered by the following eight articles of this first volume of Transactions on Rough Sets. They can be categorized into the following groups: â€“ music processing, â€“ rough set theory applied to software design models and inductive learning programming, â€“ environmental engineering models, â€“ medical data processing, â€“ pattern recognition and classification. These papers exemplify analysis and exploration of complex data sets from various domains. They provide useful insight into analyzed problems, showing for example how to compute decision rules from incomplete data. We believe that readers of this volume will better appreciate rough set theory-related trends after reading the case studies. Written for: Researchers and professionals},
keywords = {classification, data analysis, fuzzy sets, incomplete information processing, logical foundations, music recognition, pattern recognition, relational learning, rough multisets, rough non-deterministic information, rough set theory (RST), rough sets, theory of evidence, uncertainty, vagueness, },
}