An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
This work addresses the challenge of maintaining stable and continuous clustering in dynamic data streams, which is crucial for applications like real-time data analysis, but it appears incremental as it builds on existing ART-based methods with a novel parameter adaptation mechanism.
The paper tackles the problem of clustering in both stationary and nonstationary data environments by proposing an Adaptive Resonance Theory-based algorithm that autonomously adjusts parameters to adapt to distributional shifts while preserving learned structures. Experiments on 24 real-world datasets show it outperforms state-of-the-art methods in clustering performance and continual learning capability, effectively mitigating catastrophic forgetting.
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