Granular Fuzzy And Soft Computing

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Granular, Fuzzy, and Soft Computing

Granular, Fuzzy, and Soft Computing
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Publisher : Springer
Total Pages : 0
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ISBN-10 : 1071626272
ISBN-13 : 9781071626276
Rating : 4/5 (276 Downloads)

Book Synopsis Granular, Fuzzy, and Soft Computing by : Tsau-Young Lin

Download or read book Granular, Fuzzy, and Soft Computing written by Tsau-Young Lin and published by Springer. This book was released on 2023-03-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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