自适应调整参数的聚类算法，有效防止动态数据中的遗忘问题。
An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
- 基于自适应共振理论，自动调节重计算间隔和警戒阈值。
- 在24个真实数据集上表现优于现有方法，聚类稳定性更强。
- 适合处理持续学习场景下的数据流聚类任务。
静态与非静态环境下聚类要求模型能适应数据分布变化，同时保持已学聚类结构。本文提出一种基于自适应共振理论（ART）的拓扑聚类算法，通过多样性驱动的自适应机制，自主调整重计算周期和警戒阈值。该机制实现无超参数学习，在动态环境中维持聚类稳定性和连续性。在24个真实数据集上的实验表明，该算法在聚类性能与持续学习能力方面均优于当前最优方法。结果验证了所提参数自适应机制在缓解灾难性遗忘、保持演化数据流中一致聚类方面的有效性。源代码见 https://github.com/Masuyama-lab/IDAT。
原文摘要 · Abstract (English)
Clustering in stationary and nonstationary settings, where data distributions remain static or evolve over time, requires models that can adapt to distributional shifts while preserving previously learned cluster structures. This paper proposes an Adaptive Resonance Theory (ART)-based topological clustering algorithm that autonomously adjusts its recalculation interval and vigilance threshold through a diversity-driven adaptation mechanism. This mechanism enables hyperparameter-free learning that maintains cluster stability and continuity in dynamic environments. Experiments on 24 real-world datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods in both clustering performance and continual learning capability. These results highlight the effectiveness of the proposed parameter adaptation in mitigating catastrophic forgetting and maintaining consistent clustering in evolving data streams. Source code is available at https://github.com/Masuyama-lab/IDAT
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