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Clustering algorithm with strength of connectedness for m-polar fuzzy network models
Muhammad Akram1, Saba Siddique1, Majed G Alharbi2
1Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan.
This study introduces the strong degree and strength sequence for m-polar fuzzy graphs, enhancing graph theory. A novel clustering method is presented, with applications in company data analysis.
Area of Science:
- Graph Theory
- Fuzzy Mathematics
- Computer Science
Background:
- M-polar fuzzy graphs extend fuzzy graph theory with multiple membership degrees.
- Understanding vertex properties and connectivity is crucial for graph analysis.
Purpose of the Study:
- Introduce and analyze the strong degree and strength sequence in m-polar fuzzy graphs.
- Investigate connectivity parameters and develop a clustering method for m-polar fuzzy graphs.
- Apply the clustering method to real-world data, such as company information.
Main Methods:
- Definition of strong degree and m-polar fuzzy strength sequence.
- Analysis of properties in complete m-polar fuzzy graphs.
- Development of a clustering algorithm based on vertex connectedness and ϵA-reachability.
Main Results:
- Established properties of strong degree and strength sequence.
- Investigated m-polar fuzzy analogue of Whitney's theorem.
- Presented an effective clustering algorithm with a comparative analysis.
Conclusions:
- The proposed concepts and methods offer new tools for analyzing m-polar fuzzy graphs.
- The clustering algorithm demonstrates applicability and effectiveness in handling uncertain information.
- This research contributes to the advancement of fuzzy graph theory and its applications.
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