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

Naoki Masuyama1; Narito Amako2; Yuna Yamada2; Yusuke Nojima1; Hisao Ishibuchi3

2022

10.1109/ACCESS.2022.3186479

IEEE Access   影响因子和分区

2169-3536

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.

Adaptive resonance theory topological clustering hierarchical clustering continual learning

SCI ; EI

英语

其他

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]

Computer Science ; Engineering ; Telecommunications

Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000819824100001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20222812350462

Big data ; Cluster analysis ; Clustering algorithms ; Learning algorithms ; Resonance ; Topology

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

Web of Science

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

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.

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.

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.