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Topics in rough set theory : current applications to granular computing

Author: Seiki Akama; Yasuo Kudo; Tetsuya Murai
Publisher: Cham : Springer, 2020.
Series: Intelligent systems reference library.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Akama, Seiki.
Topics in Rough Set Theory : Current Applications to Granular Computing.
Cham : Springer, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Seiki Akama; Yasuo Kudo; Tetsuya Murai
ISBN: 9783030295660 3030295664
OCLC Number: 1119636445
Notes: 7.2.1 Decision Tables and Lower and Upper Approximations
Description: 1 online resource (208 pages)
Contents: 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 Recommendation Method by Directly Setting of Preference Patterns4.3.3 A Prototype of Recommender System; 4.4 Experiments; 4.4.1 Pre-experiment; 4.4.2 Evaluation Experiment; 4.5 Conclusions; References; 5 Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables; 5.1 Introduction; 5.2 Rough Sets for Incomplete Decision Tables; 5.2.1 Decision Tables and Similarity Relations; 5.2.2 Relative Reducts and Decision Rules; 5.3 Interrelationship Mining for Complete Decision Tables; 5.3.1 Observations and Motivations 5.3.2 Interrelationships Between Attributes and Indiscernibiilty of Objects by Interrelationships5.3.3 Decision Tables for Interrelationship Mining; 5.4 Interrelationships Between Attributes in Incomplete Decision Tables; 5.4.1 Three Cases in Which Interrelationships are Not Available by Null Value; 5.4.2 Similarity Relation by Interrelationship Between Attributes; 5.5 Interrelated Attributes for Incomplete Decision Tables; 5.6 Conclusions; A Proofs of Theoretical Results; References; 6 A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables 6.1 Introduction6.2 Rough Sets; 6.2.1 Decision Tables and Indiscernibility Relations; 6.2.2 Relative Reducts; 6.2.3 Discernibility Matrix; 6.2.4 Heuristic Attribute Reduction Using Reduced Decision Tables; 6.3 Parallel Computation of Heuristic Attribute Reduction; 6.3.1 OpenMP; 6.3.2 Parallelization of Heuristic Attribute Reduction; 6.4 Experiments; 6.4.1 Methods; 6.4.2 Experiment Results; 6.5 Discussion; 6.6 Conclusion and Future Issues; References; 7 Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes; 7.1 Introduction; 7.2 Rough Set Theory
Series Title: Intelligent systems reference library.
Responsibility: Seiki Akama, Yasuo Kudo, Tatsuya Murai.

Abstract:

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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