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

翻译：通常，基于自适应共振理论（ART）的算法中，节点学习过程的相似度阈值（即警戒参数）对聚类性能具有显著影响。此外，拓扑聚类算法中的边删除阈值在自组织过程中对自适应生成良好分离的簇起着重要作用。本文提出一种基于ART的拓扑聚类算法，该算法整合了相似度阈值与边删除阈值的参数估计方法。相似度阈值通过基于行列式点过程的准则进行估计，而边删除阈值则依据边的寿命进行定义。在合成数据集与真实数据集上的实验结果表明，所提算法在不需针对数据集进行特定参数设定的情况下，其聚类性能优于当前最先进的聚类算法。源代码可在 https://github.com/Masuyama-lab/CAE 获取。