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

• URL: http://arxiv.org/abs/2305.01507v2
• Date: Wed, 3 May 2023 01:58:25 GMT
• Title: A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
• Authors: Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi, Stefan Wermter
• Abstract summary: We propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications.
• Score: 20.995946115633963
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications.

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