Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments
Narito Amako, Naoki Masuyama, Chu Kiong Loo, Yusuke Nojima, Yiping Liu, Hisao Ishibuchi · 2020
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.