Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

翻译：适应共振理论(ART)被认为是实现持续学习的有效方法,因为它有能力处理可塑性稳定难题,但一般而言,基于ART的算法的集群性能在很大程度上取决于类似性临界值的规格,即以数据为依存和手法指定的警戒参数。本文建议采用基于ART的地形群集算法,其机制可自动估计与数据点分布相近的临界值。此外,为了改进信息提取性能,通过对拟议的算法采用等级结构,提出了能够持续学习的分裂性等级组合算法。实验结果表明,拟议的算法具有与最近提出的最先进的等级群算法相类似的高集群性能。