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[2305.01507] A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Summary
This article presents a novel parameter-free Adaptive Resonance Theory-based topological clustering algorithm that enhances clustering performance without requiring dataset-specific parameters.
Why It Matters
The proposed algorithm addresses a significant challenge in clustering by eliminating the need for parameter tuning, which can be a barrier for practitioners. Its capability for continual learning makes it particularly relevant in dynamic data environments, where adaptability is crucial.
Key Takeaways
- Introduces a parameter-free clustering algorithm that improves performance.
- Utilizes a determinantal point process for similarity threshold estimation.
- Incorporates edge age for adaptive edge deletion, enhancing cluster separation.
- Demonstrates superior results on both synthetic and real-world datasets.
- Eliminates the need for dataset-specific parameter tuning, simplifying application.
Computer Science > Neural and Evolutionary Computing arXiv:2305.01507 (cs) [Submitted on 1 May 2023 (v1), last revised 19 Feb 2026 (this version, v3)] 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 View a PDF of the paper titled A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning, by Naoki Masuyama and 5 other authors View PDF HTML (experimental) 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 ...
