Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 10th Int
This volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005. This confe
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Subseries of Lecture Notes in Computer Science
3642
´ ezak JingTao Yao Dominik Sl¸ James F. Peters Wojciech Ziarko Xiaohua Hu (Eds.)
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 10th International Conference, RSFDGrC 2005 Regina, Canada, August 31 – September 3, 2005 Proceedings, Part II
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors ´ ezak Dominik Sl¸ JingTao Yao Wojciech Ziarko University of Regina, Department of Computer Science 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada E-mail: {slezak, jtyao, ziarko}@cs.uregina.ca James F. Peters University of Manitoba, Department of Electrical and Computer Engineering Winnipeg, MB R3T 5V6, Canada E-mail: [email protected] Xiaohua Hu Drexel University, College of Information Science and Technology Philadelphia, PA 19104, USA E-mail: [email protected]
Library of Congress Control Number: 2005931253
CR Subject Classification (1998): I.2, H.2.4, H.3, F.4.1, F.1, I.5, H.4 ISSN ISBN-10 ISBN-13
0302-9743 3-540-28660-8 Springer Berlin Heidelberg New York 978-3-540-28660-8 Springer Berlin Heidelberg New York
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Preface
This volume contains the papers selected for presentation at the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005. This conference followed in the footsteps of international events devoted to the subject of rough sets, held so far in Canada, China, Japan, Poland, Sweden, and the USA. RSFDGrC achieved the status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. The main idea is based on indiscernibility relations that describe indistinguishability of objects. Concepts are represented by approximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas such as finance, industry, multimedia, and medicine. T
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