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

arXiv: https://arxiv.org/abs/2511.17983 · HTML: https://mlsanity.com/paper/2511.17983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 24 of 24)

- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Class-Incremental Learning: A Survey](https://mlsanity.com/paper/2302.03648/) (2023)
- [Efficient Parameter-free Clustering Using First Neighbor Relations](https://mlsanity.com/paper/1902.11266/) (2019)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization](https://mlsanity.com/paper/1805.10966/) (2018)
- [Deep clustering: On the link between discriminative models and K-means](https://mlsanity.com/paper/1810.04246/) (2018)
- [Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning](https://mlsanity.com/paper/2301.00802/) (2023)
- [Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence](https://mlsanity.com/paper/1901.00794/) (2018)
- [TableDC: Deep Clustering for Tabular Data](https://mlsanity.com/paper/2405.17723/) (2024)
- [Prototype-Based Continual Learning with Label-free Replay Buffer and Cluster Preservation Loss](https://mlsanity.com/paper/2504.07240/) (2025)
- [Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning](https://mlsanity.com/paper/1811.08318/) (2018)
- [Localizing Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1906.02568/) (2019)
- [Multi-label Classification via Adaptive Resonance Theory-based Clustering](https://mlsanity.com/paper/2103.01511/) (2021)
- [Map-based Experience Replay: A Memory-Efficient Solution to Catastrophic Forgetting in Reinforcement Learning](https://mlsanity.com/paper/2305.02054/) (2023)
- [Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning](https://mlsanity.com/paper/2201.10713/) (2022)
- [Continual Robot Learning using Self-Supervised Task Inference](https://mlsanity.com/paper/2309.04974/) (2023)
- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)
- [U-TELL: Unsupervised Task Expert Lifelong Learning](https://mlsanity.com/paper/2405.14623/) (2024)
- [Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory](https://mlsanity.com/paper/2309.03487/) (2023)
- [Forward-Backward Knowledge Distillation for Continual Clustering](https://mlsanity.com/paper/2405.19234/) (2024)

## Built on by (most cited first, 1 of 1)

- [PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering](https://mlsanity.com/paper/2605.08673/) (2026)

## Abstract (arXiv)

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