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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Author: Ning Zhong
Publisher: Springer
Total Pages: 587
Release: 2004-06-22
Genre: Mathematics
ISBN: 3540480617
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This book constitutes the refereed proceedings of the 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC'99, held in Yamaguchi, Japan, in November 1999. The 45 revised regular papers and 15 revised short papers presented together with four invited contributions were carefully reviewed and selected from 89 submissions. The book is divided into sections on rough computing: foundations and applications, rough set theory and applications, fuzzy set theory and applications, nonclassical logic and approximate reasoning, information granulation and granular computing, data mining and knowledge discovery, machine learning, and intelligent agents and systems.

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Author: Ning Zhong
Publisher: Springer
Total Pages: 566
Release: 1999-10-20
Genre: Mathematics
ISBN: 9783540666455
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Download New Directions in Rough Sets, Data Mining, and Granular-Soft Computing Book in PDF, ePub and Kindle

This book constitutes the refereed proceedings of the 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC'99, held in Yamaguchi, Japan, in November 1999. The 45 revised regular papers and 15 revised short papers presented together with four invited contributions were carefully reviewed and selected from 89 submissions. The book is divided into sections on rough computing: foundations and applications, rough set theory and applications, fuzzy set theory and applications, nonclassical logic and approximate reasoning, information granulation and granular computing, data mining and knowledge discovery, machine learning, and intelligent agents and systems.

Rough Set Theory and Granular Computing

Rough Set Theory and Granular Computing
Author: Masahiro Inuiguchi
Publisher: Springer
Total Pages: 303
Release: 2012-12-06
Genre: Technology & Engineering
ISBN: 3540364730
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After 20 years of pursuing rough set theory and its applications a look on its present state and further prospects is badly needed. The monograph Rough Set Theory and Granular Computing edited by Masahiro Inuiguchi, Shoji Hirano and Shusaku Tsumoto meets this demand. It presents the newest developments in this area and gives fair picture of the state of the art in this domain. Firstly, in the keynote papers by Zdzislaw Pawlak, Andrzej Skowron and Sankar K. Pal the relationship of rough sets with other important methods of data analysis -Bayes theorem, neuro computing and pattern recognitio- is thoroughly examined. Next, several interesting generalizations of the the ory and new directions of research are presented. Furthermore application of rough sets in data mining, in particular, rule induction methods based on rough set theory is presented and discussed. Further important issue dis cussed in the monograph is rough set based data analysis, including study of decisions making in conflict situations. Last but not least, some recent engi neering applications of rough set theory are given. They include a proposal of rough set processor architecture organization for fast implementation of ba sic rough set operations and discussion of results concerning advanced image processing for unmanned aerial vehicle. Thus the monograph beside presenting wide spectrum of ongoing research in this area also points out new emerging areas of study and applications, which makes it a valuable source of information to all interested in this do main.

Data Mining, Rough Sets and Granular Computing

Data Mining, Rough Sets and Granular Computing
Author: Tsau Young Lin
Publisher: Physica
Total Pages: 538
Release: 2013-11-11
Genre: Computers
ISBN: 3790817910
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During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Author: Dominik Slezak
Publisher: Springer
Total Pages: 764
Release: 2005-09-19
Genre: Computers
ISBN: 3540318259
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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 conference followed in the footsteps of inter- tional events devoted to the subject of rough sets, held so far in Canada, China, Japan,Poland,Sweden, and the USA. RSFDGrC achievedthe status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzis law 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 - proximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to signi?cant results in many areas such as ?nance, industry, multimedia, and medicine. The RSFDGrC conferences put an emphasis on connections between rough sets and fuzzy sets, granularcomputing, and knowledge discoveryand data m- ing, both at the level of theoretical foundations and real-life applications. In the case of this event, additional e?ort was made to establish a linkage towards a broader range of applications. We achieved it by including in the conference program the workshops on bioinformatics, security engineering, and embedded systems, as well as tutorials and sessions related to other application areas.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Author: Guoyin Wang
Publisher: Springer
Total Pages: 758
Release: 2003-08-03
Genre: Computers
ISBN: 354039205X
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This volume contains the papers selected for presentation at the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2003) held at Chongqing University of Posts and Telecommunications, Chongqing, P.R. China, May 26–29, 2003. There were 245 submissions for RSFDGrC 2003 excluding for 2 invited keynote papers and 11 invited plenary papers. Apart from the 13 invited papers, 114 papers were accepted for RSFDGrC 2003 and were included in this volume. The acceptance rate was only 46.5%. These papers were divided into 39 regular oral presentation papers (each allotted 8 pages), 47 short oral presentation papers (each allotted 4 pages) and 28 poster presentation papers (each allotted 4 pages) on the basis of reviewer evaluations. Each paper was reviewed by three referees. The conference is a continuation and expansion of the International Workshops on Rough Set Theory and Applications. In particular, this was the ninth meeting in the series and the first international conference. The aim of RSFDGrC2003 was to bring together researchers from diverse fields of expertise in order to facilitate mutual understanding and cooperation and to help in cooperative work aimed at new hybrid paradigms. It is our great pleasure to dedicate this volume to Prof. Zdzislaw Pawlak, who first introduced the basic ideas and definitions of rough sets theory over 20 years ago.

Rough Sets, Fuzzy Sets, Data Mining and Granular Computing

Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Author: Sergei O. Kuznetsov
Publisher: Springer Science & Business Media
Total Pages: 382
Release: 2011-06-14
Genre: Computers
ISBN: 3642218806
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This book constitutes the refereed proceedings of the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2011, held in Moscow, Russia in June 2011. The 49 revised full papers presented together with 5 invited and 2 tutorial papers were carefully reviewed and selected from a total of 83 submissions. The papers are organized in topical sections on rough sets and approximations, coverings and granules, fuzzy set models, fuzzy set applications, compound values, feature seletion and reduction, clusters and concepts, rules and trees, image processing, and interactions and visualization.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Author: Davide Ciucci
Publisher: Springer
Total Pages: 412
Release: 2013-10-07
Genre: Computers
ISBN: 3642412181
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This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of the co-located conference of the 2013 Joint Rough Set Symposium, JRS 2013. The 69 papers (including 44 regular and 25 short papers) included in the JRS proceedings (LNCS 8170 and LNCS 8171) were carefully reviewed and selected from 106 submissions. The papers in this volume cover topics such as inconsistency, incompleteness, non-determinism; fuzzy and rough hybridization; granular computing and covering-based rough sets; soft clustering; image and medical data analysis.