Topics in rough set theory: current applications to granular computing

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This book discusses current topics in rough set theory. Since Pawlaks 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.

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Title Statements

Full title :Topics in rough set theory : current applications to granular computing / Seiki Akama, Yasuo Kudo, Tatsuya Murai.

Summary

Edition Statements

Publication Date:2020.

Publication Statements

Publisher(s):Springer
Place of Publication:Cham
Language:eng
Publication Date:2020.

Notes

7.2.1 Decision Tables and Lower and Upper Approximations

The Physical Object

Description:1 online resource (208 pages)

Edition Identifiers

ISBN:9783030295660, 3030295664
oclc:on1119636445
LC Classification:QA248
Dewey Decimal:551.322

Intro; Foreword; Preface; Contents; 1 Introduction; 1.1 Backgrounds; 1.2 About This Book; References; 2 Overview of Rough Set Theory; 2.1 Rough Sets; 2.2 Algebras, Logics and Rough Sets; 2.3 Modal Logic and Rough Sets; 2.4 Rough Set Logics; 2.5 Logics for Reasoning About Knowledge; 2.6 Logics for Knowledge Representation; 2.7 Fuzzy Logic; 2.8 Applications of Rough Set Theory; References; 3 Object Reduction in Rough Set Theory; 3.1 Introduction; 3.2 Rough Sets; 3.2.1 Decision Tables, Indiscernibility Relations, and Lower Approximations; 3.2.2 Relative Reducts; 3.2.3 Discernibility Matrix

3.3 Proposal of Object Reduction3.3.1 Definition of Object Reduction; 3.3.2 Properties of Possibly Reducible Objects and Irreducible Objects; 3.3.3 An Algorithm for Object Reduction; 3.3.4 Application to Dataset; 3.4 Conclusions; References; 4 Recommendation Method by Direct Setting of Preference Patterns Based on Interrelationship Mining; 4.1 Introduction; 4.2 Background; 4.2.1 Recommender Systems; 4.2.2 Rough Set Theory; 4.2.3 Rough-Set-Based Interrelationship Mining; 4.2.4 Yamawaki et al.'s Recommendation Method; 4.3 Proposed Method; 4.3.1 Direct Setting of Preference Patterns

4.3.2 Recommenda…