Granular Computing Using Information Tables

2002 ◽  
pp. 102-124 ◽  
Author(s):  
Y. Y. Yao ◽  
Ning Zhong
Keyword(s):  
Granular Computing ◽  
Information Tables

Granular computing on information tables: Families of subsets and operators

2018 ◽  
Vol 442-443 ◽  
pp. 72-102 ◽  
Author(s):  
G. Chiaselotti ◽  
T. Gentile ◽  
F. Infusino
Keyword(s):  
Granular Computing ◽  
Information Tables ◽  
Families Of Subsets

Conversion-based aggregation algorithms for linear ordinal rankings combined with granular computing

2021 ◽  
Vol 219 ◽  
pp. 106880
Author(s):  
Nana Liu ◽  
Zeshui Xu ◽  
Hangyao Wu ◽  
Peijia Ren
Keyword(s):  
Granular Computing

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

2004 ◽  
Vol 12 (01) ◽  
pp. 37-46 ◽  
Author(s):  
JIYE LIANG ◽  
ZHONGZHI SHI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Mathematical Tool

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

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