Complex shadowed set theory and its application in decision-making problems

Document Type

Article

Source of Publication

AIMS Mathematics

Publication Date

1-1-2024

Abstract

Modern technology makes it easier to store datasets, but extracting and isolating useful information with its full meaning from this data is crucial and hard. Recently, several algorithms for clustering data have used complex fuzzy sets (CFS) to improve clustering performance. Thus, adding a second dimension (phase term) to the range of membership avoids the problem of losing the full meaning of complicated information during the decision-making process. In this research, the notion of the complex shadowed set (CSHS) was introduced and considered as an example of the three region approximations method simplifying processing with the support of CFS and improving the representation of results attained within. This notion can be founded by extending the shadowed set codomain from {0, [0, 1], 1} into {0eiθ, [0, 1]eiθ, 1eiθ}. The significance of CSHS was illustrated by giving an example. Additionally, some properties of the CSHS were examined. The basic CSHS operations, complement, union, and intersection were investigated with their properties. Finally, an application in decision-making was illuminated to support the present notion.

ISSN

2473-6988

Publisher

American Institute of Mathematical Sciences (AIMS)

Volume

9

Issue

6

First Page

16810

Last Page

16825

Disciplines

Physical Sciences and Mathematics

Keywords

complex fuzzy sets, complex shadowed sets, fuzzy sets, shadowed sets, thresholds approximation

Scopus ID

85193291803

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

This document is currently not available here.

Share

COinS