Topics in rough set theory : current applications to granular computing /: current applications to granular computing. (2020)

• Record Type: Book
• Further Information: Note: Seiki Akama, Yasuo Kudo, Tatsuya Murai.
• Other Names: Akama, Seiki; Kudo, Yasuo; Murai, Tetsuya
• 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
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource (208 pages)
• Subjects: 551.322; Rough sets; Granular computing; Granular computing; Rough sets; Electronic books
• Languages: English
• ISBNs: 9783030295660; 3030295664
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed September 25, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.