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Topics in Rough Set Theory

Current Applications to Granular Computing

  • Book
  • © 2020

Overview

Authors:
  1. Seiki Akama

    1. C-Republic, Kawasaki, Japan

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    You can also search for this author in PubMed   Google Scholar

  2. Yasuo Kudo

    1. Muroran Institute of Technology, Muroran, Japan

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    You can also search for this author in PubMed   Google Scholar

  3. Tetsuya Murai

    1. Chitose Institute of Science and Technology, Chitose, Japan

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    You can also search for this author in PubMed   Google Scholar

  • Examines current topics in rough set theory
  • Discusses recent research issues, including object-oriented rough set models, recommendation systems, decision tables, and granular computing
  • Includes current applications in granular computing

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 168)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 1-5

  3. Overview of Rough Set Theory

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 7-60

  4. Object Reduction in Rough Set Theory

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 61-70

  5. Recommendation Method by Direct Setting of Preference Patterns Based on Interrelationship Mining

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 71-79

  6. Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 81-99

  7. A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 101-111

  8. Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 113-127

  9. An Evaluation Method of Relative Reducts Based on Roughness of Partitions

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 129-140

  10. Neighbor Selection for User-Based Collaborative Filtering Using Covering-Based Rough Sets

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 141-159

  11. Granular Computing and Aristotle’s Categorical Syllogism

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 161-172

  12. A Modal Characterization of Visibility and Focus in Granular Reasoning

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 173-185

  13. Directions for Future Work in Rough Set Theory

    • Seiki Akama, Yasuo Kudo, Tetsuya Murai
    Pages 187-198

  14. Back Matter

    Pages 199-201
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Keywords

About this book

This book discusses current topics in rough set theory.  Since Pawlak’s rough set theory was first proposed to offer a basis for imprecise and uncertain data and reasoning from data, many workers have investigated its foundations and applications. Examining various topical issues, including object-oriented rough set models, recommendation systems, decision tables, and granular computing, the book is a valuable resource for students and researchers in the field.



Authors and Affiliations

  • C-Republic, Kawasaki, Japan

    Seiki Akama

  • Muroran Institute of Technology, Muroran, Japan

    Yasuo Kudo

  • Chitose Institute of Science and Technology, Chitose, Japan

    Tetsuya Murai

Bibliographic Information

  • Book Title: Topics in Rough Set Theory

  • Book Subtitle: Current Applications to Granular Computing

  • Authors: Seiki Akama, Yasuo Kudo, Tetsuya Murai

  • Series Title: Intelligent Systems Reference Library

  • DOI: https://doi.org/10.1007/978-3-030-29566-0

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-29565-3Published: 19 September 2019

  • Softcover ISBN: 978-3-030-29568-4Published: 19 September 2020

  • eBook ISBN: 978-3-030-29566-0Published: 10 September 2019

  • Series ISSN: 1868-4394

  • Series E-ISSN: 1868-4408

  • Edition Number: 1

  • Number of Pages: XV, 201

  • Number of Illustrations: 13 b/w illustrations, 1 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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