Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 30, Issue 9, Pages 3473-3485Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3117442
Keywords
Complex network; computational intelligence; data science; fuzzy clustering; generalized momentum
Funding
- Natural Science Foundation of Xinjiang Uygur Autonomous Region [2021D01D05]
- National Natural Science Foundation of China [61772493]
- Natural Science Foundation of Chongqing (China) [cstc2019jcyjjqX0013]
- Chongqing Research Program of Technology Innovation and Application [cstc2018jcyjAX0703]
- Pioneer Hundred Talents Program of the Chinese Academy of Sciences
- CAAI-Huawei MindSpore Open Fund [CAAIXSJLJJ-2020-004B]
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This article proposes a fast fuzzy clustering algorithm called F(2)CAN, which incorporates a generalized momentum method into FCAN to address the slow convergence issue, achieving better performance in empirical studies.
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(2)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(2)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(2)CAN has potential for addressing the clustering analysis of large-scale complex networks emerging from industrial applications.
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