Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments

Amako，Narito1; Masuyama，Naoki1; Loo，Chu Kiong2; Nojima，Yusuke1; Liu，Yiping1; Ishibuchi，Hisao3

10.1109/IJCNN48605.2020.9207071

2020-07-01

2161-4393

978-1-7281-6927-9

Proceedings of the International Joint Conference on Neural Networks

1-8

19-24 July 2020

Glasgow, UK

Clustering based on Adaptive Resonance Theory (ART) has been actively studied. In previous studies, ART-based clustering algorithms with a topological structure have been proposed and showed their superior self-organizing ability. However, this method deteriorates the clustering performance at high noise ratios. In this paper, we propose a multilayer clustering algorithm based on a topological ART-based clustering for improving a noise reduction ability. Simulation experiments show that the proposed algorithm achieves excellent clustering performance on a 2D synthetic dataset in high noise environments.

Adaptive Resonance Theory (ART) Correntropy Multilayer Clustering Noisy Environment

通讯

英语

EI

20204409409628

Multilayers ; Resonance ; Noise abatement ; Topology

Acoustic Noise:751.4 ; Information Sources and Analysis:903.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Mechanics:931.1

2-s2.0-85093830996

Scopus

1.Osaka Prefecture University,Department of Computer Science and Intelligent Systems,Osaka,Japan
2.University of Malaya,Faculty of Computer Science and Information Technology,Kuala Lumpur,Malaysia
3.Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Amako，Narito,Masuyama，Naoki,Loo，Chu Kiong,et al. Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments[C],2020:1-8.