A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning

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 this https URL: https://github.com/Masuyama-lab/CAE

@Article{MTNLIW26,
 	 author =  {Masuyama, Naoki and Takebayashi, Takanori and Nojima, Yusuke and Loo, Chu Kiong and Ishibuchi, Hisao and Wermter, Stefan},
 	 title = {A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning},
 	 booktitle = {}
 	 journal = {Neural Computing and Applications},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {}
 	 year = {2026},
 	 month = {Feb},
 	 publisher = {}
 	 doi = {10.48550/arXiv.2305.01507},
 }