A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Abstract
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose an ART-based topological clustering algorithm that integrates parameter estimation methods for both the similarity threshold and the edge deletion threshold. The similarity threshold is estimated using a determinantal point process-based criterion, while the edge deletion threshold is defined based on the age of edges. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art clustering algorithms without requiring parameter specifications specific to the datasets. Source code is available at https://github.com/Masuyama-lab/CAE
Cite
@article{arxiv.2305.01507,
title = {A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning},
author = {Naoki Masuyama and Takanori Takebayashi and Yusuke Nojima and Chu Kiong Loo and Hisao Ishibuchi and Stefan Wermter},
journal= {arXiv preprint arXiv:2305.01507},
year = {2026}
}
Comments
This paper is accepted to Neural Computing and Applications