中文版 | English
Title

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Author
Publication Years
2022
DOI
Source Title
ISSN
2169-3536
Volume10Pages:68042-68056
Abstract
Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.
Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China[61876075] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07 x 386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]
WOS Research Area
Computer Science ; Engineering ; Telecommunications
WOS Subject
Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS Accession No
WOS:000819824100001
Publisher
EI Accession Number
20222812350462
EI Keywords
Big data ; Cluster analysis ; Clustering algorithms ; Learning algorithms ; Resonance ; Topology
ESI Classification Code
Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Machine Learning:723.4.2 ; Information Sources and Analysis:903.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Mechanics:931.1
Data Source
Web of Science
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9807317
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/347912
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Graduate School of Informatics, Osaka Metropolitan University, Sakai-shi, Osaka, Japan
2.Graduate School of Engineering, Osaka Prefecture University, Sakai-shi, Osaka, Japan
3.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
Recommended Citation
GB/T 7714
Naoki Masuyama,Narito Amako,Yuna Yamada,et al. Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning[J]. IEEE Access,2022,10:68042-68056.
APA
Naoki Masuyama,Narito Amako,Yuna Yamada,Yusuke Nojima,&Hisao Ishibuchi.(2022).Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning.IEEE Access,10,68042-68056.
MLA
Naoki Masuyama,et al."Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning".IEEE Access 10(2022):68042-68056.
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