Faisal Aburub
Biographic Data
| ID | 6956438 |
|---|---|
| NAME | Faisal Aburub |
| GIVEN NAMES | Faisal |
| FAMILY NAME | Aburub |
| SIGNATURE | ABURUB F |
| AFFILIATIONS | Petra University |
| ORCID | 0000-0002-6461-9836 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
No prominent works on this page.
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Computer Science (4 works) · Fuzzy logic (4 works) · Algebra over a field (2 works) · Algebraic number (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Business (2 works) · Cluster analysis (2 works) · Cognitive Computing and Networks (2 works) · Complex Systems and Time Series Analysis (2 works)