A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913104956227584 |
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| author | Masuyama, Naoki Takebayashi, Takanori Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Wermter, Stefan |
| author_facet | Masuyama, Naoki Takebayashi, Takanori Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Wermter, Stefan |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_01507 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning Masuyama, Naoki Takebayashi, Takanori Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Wermter, Stefan Neural and Evolutionary Computing Machine 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 https://github.com/Masuyama-lab/CAE |
| title | A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2305.01507 |