A Fast Fuzzy Clustering Algorithm for Complex Networks via a Generalized Momentum Method

Complex networks have been widely adopted to represent a variety of complicated systems. Given a complex network, it is of great significance to perform accurate clustering for better understanding its intrinsic organization. To this end, a fuzzy-based clustering algorithm, i.e., FCAN, has been deve...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on fuzzy systems Ročník 30; číslo 9; s. 3473 - 3485
Hlavní autoři: Hu, Lun, Pan, Xiangyu, Tang, Zehai, Luo, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Analytical models
Cluster analysis
Clustering
Clustering algorithms
Complex network
Complex networks
computational intelligence
Convergence
Data science
Empirical analysis
fuzzy clustering
generalized momentum
Industrial applications
Linear programming
Momentum
Networks
Optimization
Topology
ISSN:1063-6706, 1941-0034
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Complex networks have been widely adopted to represent a variety of complicated systems. Given a complex network, it is of great significance to perform accurate clustering for better understanding its intrinsic organization. To this end, a fuzzy-based clustering algorithm, i.e., FCAN, has been developed. Though effective, FCAN suffers from the disadvantage of slow convergence, which in return constrains its efficiency. To address this issue, this article proposes a fast fuzzy clustering algorithm, namely, F <inline-formula><tex-math notation="LaTeX">^2</tex-math></inline-formula>CAN, which incorporates a generalized momentum method into FCAN. Its fast convergence is rigorous justified in theory. Empirical studies on five datasets from real applications demonstrate that F <inline-formula><tex-math notation="LaTeX">^2</tex-math></inline-formula>CAN achieves a better performance when compared with FCAN and several state-of-the-art clustering algorithms in terms of convergence rate and clustering accuracy simultaneously. Hence, F <inline-formula><tex-math notation="LaTeX">^2</tex-math></inline-formula>CAN has potential for addressing the clustering analysis of large-scale complex networks emerging from industrial applications.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2021.3117442