Granular Computing with Shadowed Sets

2005 ◽  
pp. 23-32 ◽  
Author(s):  
Witold Pedrycz
Keyword(s):  
Granular Computing ◽  
Shadowed Sets

Synergy of Granular Computing, Shadowed Sets, and Three-way Decisions

2020 ◽  
Vol 508 ◽  
pp. 422-425 ◽  
Author(s):  
Davide Ciucci ◽  
Yiyu Yao
Keyword(s):  
Granular Computing ◽  
Shadowed Sets

FEATURE WEIGHTING FUZZY CLUSTERING INTEGRATING ROUGH SETS AND SHADOWED SETS

2012 ◽  
Vol 26 (04) ◽  
pp. 1250010 ◽  
Author(s):  
LINA WANG ◽  
JIANDONG WANG
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Clustering ◽  
Rough Sets ◽  
Granular Computing ◽  
Clustering Algorithms ◽  
Noisy Data ◽  
Real Data ◽  
Feature Weighting ◽  
Novel Method ◽  
Shadowed Sets

Associating features with weights is a common approach in clustering algorithms and determining the weight values is crucial in generating valid partitions. In this paper, we introduce a novel method in the framework of granular computing that incorporates fuzzy sets, rough sets and shadowed sets, and calculates feature weights automatically. Experiments on synthetic and real data patterns show that our algorithms always converge and are more effective in handling overlapping among clusters and more robust in the presence of noisy data and outliers.

Granular computing with shadowed sets

2002 ◽  
Vol 17 (2) ◽  
pp. 173-197 ◽  
Author(s):  
Witold Pedrycz ◽  
George Vukovich
Keyword(s):  
Granular Computing ◽  
Shadowed Sets

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.

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