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

2025-11-22

AI Summary

This paper addresses continual clustering in both stationary and non-stationary data streams. We propose an incremental clustering algorithm that integrates Adaptive Resonance Theory (ART) with topological structure modeling. Its core contribution is a diversity-driven adaptive mechanism that autonomously and dynamically tunes the vigilance parameter and recomputation interval—eliminating manual intervention. This mechanism effectively mitigates catastrophic forgetting and ensures clustering stability and structural consistency under distributional shifts. The algorithm operates in a fully unsupervised, online learning setting, achieving both high static accuracy and strong dynamic robustness. Extensive experiments across 24 real-world datasets demonstrate that our method significantly outperforms existing state-of-the-art approaches in both clustering accuracy and continual learning stability. The source code is publicly available.

Technology Category

• Machine Learning: Clustering
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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

Problem

• Adapts clustering to static and evolving data distributions over time
• Autonomously adjusts vigilance threshold and recalculation interval parameters
• Mitigates catastrophic forgetting while maintaining cluster stability in dynamic environments

Innovation

• ART-based topological clustering with self-adjusting vigilance
• Diversity-driven adaptation enables hyperparameter-free learning
• Autonomous recalculation interval adjustment for dynamic environments