An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter

TL;DR

This paper introduces an ART-based topological clustering algorithm with a self-adjusting vigilance parameter, enabling adaptive, hyperparameter-free learning in dynamic data environments, outperforming existing methods in stability and continual learning.

Contribution

The proposed algorithm autonomously adjusts its parameters through a diversity-driven mechanism, enhancing clustering stability and continual learning without manual hyperparameter tuning.

Findings

Outperforms state-of-the-art clustering methods on real-world datasets.

Effectively mitigates catastrophic forgetting in evolving data streams.

Maintains consistent cluster structures in nonstationary environments.

Abstract

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