Akama S., Kudo Y., Murai T. Topics in Rough Set Theory: Current Applications to Granular Computing

Springer, 2020. — 208 p. — (Intelligent Systems Reference Library 168). — ISBN: 978-3-030-29565-3.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.Overview of Rough Set Theory
Object Reduction in Rough Set Theory
Recommendation Method by Direct Setting of Preference Patterns Based on Interrelationship Mining
Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables
A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables
Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes
An Evaluation Method of Relative Reducts Based on Roughness of Partitions
Neighbor Selection for User-Based Collaborative Filtering Using Covering-Based Rough Sets
Granular Computing and Aristotle’s Categorical Syllogism
A Modal Characterization of Visibility and Focus in Granular Reasoning
Directions for Future Work in Rough Set Theory