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Granular, fuzzy, and soft computing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Granular, fuzzy, and soft computingedited by Tsau-Young Lin, Churn-Jung Liau, Janusz Kacprzyk.
其他作者:	Lin, Tsau-Young.
出版者:	New York, NY :Springer US :2023.
面頁冊數:	xxxi, 933 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eReference
標題:	Granular computing.
電子資源:	https://doi.org/10.1007/978-1-0716-2628-3
ISBN:	9781071626283$q(electronic bk.)

Granular, fuzzy, and soft computing
Granular, fuzzy, and soft computing[electronic resource] /edited by Tsau-Young Lin, Churn-Jung Liau, Janusz Kacprzyk. - New York, NY :Springer US :2023. - xxxi, 933 p. :ill., digital ;24 cm. - Encyclopedia of complexity and systems science series,2629-2343. - Encyclopedia of complexity and systems science series..

Cooperative Multi-hierarchical Query Answering Systems -- Dependency and Granularity in Data -Mining -- Fuzzy Logic -- Fuzzy Probability Theory -- Fuzzy System Models Evolution from Fuzzy Rulebases to Fuzzy Functions -- On Genetic-Fuzzy Data Mining Techniques -- Granular Computing and Data Mining for Ordered Data: The Dominance-Based Rough Set Approach -- Granular Computing, Information Models for -- Granular Computing, Introduction to -- Granular Computing and Modeling of the Uncertainty in Quantum Mechanics -- Philosophical Foundation for Granular Computing -- Granular Computing: Practices, Theories, and Future Directions -- Granular Computing, Principles and Perspectives of -- Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities -- Granular Model for Data Mining -- Granular Neural Networks -- Granulation of Knowledge: Similarity Based Approach in Information and Decision Systems -- Multi-Granular Computing and Quotient Structure -- Non-standard Analysis, An Invitation to -- Information System Design Using Fuzzy and Rough Set Theory -- Rough Set Data Analysis.

The first edition of the Encyclopedia of Complexity and Systems Science (ECSS, 2009) presented a comprehensive overview of granular computing (GrC) broadly divided into several categories: Granular computing from rough set theory, Granular Computing in Database Theory, Granular Computing in Social Networks, Granular Computing and Fuzzy Set Theory, Grid/Cloud Computing, as well as general issues in granular computing. In 2011, the formal theory of GrC was established, providing an adequate infrastructure to support revolutionary new approaches to computer/data science, including the challenges presented by so-called big data. For this volume of ECSS, Second Edition, many entries have been updated to capture these new developments, together with new chapters on such topics as data clustering, outliers in data mining, qualitative fuzzy sets, and information flow analysis for security applications. Granulations can be seen as a natural and ancient methodology deeply rooted in the human mind. Many daily "things" are routinely granulated into sub "things": The topography of earth is granulated into hills, plateaus, etc., space and time are granulated into infinitesimal granules, and a circle is granulated into polygons of infinitesimal sides. Such granules led to the invention of calculus, topology and non-standard analysis. Formalization of general granulation was difficult but, as shown in this volume, great progress has been made in combing discrete and continuous mathematics under one roof for a broad range of applications in data science.

ISBN: 9781071626283$q(electronic bk.)

Standard No.: 10.1007/978-1-0716-2628-3doiSubjects--Topical Terms:

224958
Granular computing.

LC Class. No.: QA76.9.S63

Dewey Class. No.: 006.3

Granular, fuzzy, and soft computing
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505 0 $a Cooperative Multi-hierarchical Query Answering Systems -- Dependency and Granularity in Data -Mining -- Fuzzy Logic -- Fuzzy Probability Theory -- Fuzzy System Models Evolution from Fuzzy Rulebases to Fuzzy Functions -- On Genetic-Fuzzy Data Mining Techniques -- Granular Computing and Data Mining for Ordered Data: The Dominance-Based Rough Set Approach -- Granular Computing, Information Models for -- Granular Computing, Introduction to -- Granular Computing and Modeling of the Uncertainty in Quantum Mechanics -- Philosophical Foundation for Granular Computing -- Granular Computing: Practices, Theories, and Future Directions -- Granular Computing, Principles and Perspectives of -- Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities -- Granular Model for Data Mining -- Granular Neural Networks -- Granulation of Knowledge: Similarity Based Approach in Information and Decision Systems -- Multi-Granular Computing and Quotient Structure -- Non-standard Analysis, An Invitation to -- Information System Design Using Fuzzy and Rough Set Theory -- Rough Set Data Analysis.
520 $a The first edition of the Encyclopedia of Complexity and Systems Science (ECSS, 2009) presented a comprehensive overview of granular computing (GrC) broadly divided into several categories: Granular computing from rough set theory, Granular Computing in Database Theory, Granular Computing in Social Networks, Granular Computing and Fuzzy Set Theory, Grid/Cloud Computing, as well as general issues in granular computing. In 2011, the formal theory of GrC was established, providing an adequate infrastructure to support revolutionary new approaches to computer/data science, including the challenges presented by so-called big data. For this volume of ECSS, Second Edition, many entries have been updated to capture these new developments, together with new chapters on such topics as data clustering, outliers in data mining, qualitative fuzzy sets, and information flow analysis for security applications. Granulations can be seen as a natural and ancient methodology deeply rooted in the human mind. Many daily "things" are routinely granulated into sub "things": The topography of earth is granulated into hills, plateaus, etc., space and time are granulated into infinitesimal granules, and a circle is granulated into polygons of infinitesimal sides. Such granules led to the invention of calculus, topology and non-standard analysis. Formalization of general granulation was difficult but, as shown in this volume, great progress has been made in combing discrete and continuous mathematics under one roof for a broad range of applications in data science.
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