# A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning

arXiv: https://arxiv.org/abs/2305.01507 · HTML: https://mlsanity.com/paper/2305.01507/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 70 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 21 of 21)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [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 Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Effective Diversity in Population Based Reinforcement Learning](https://mlsanity.com/paper/2002.00632/) (2020)
- [A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks](https://mlsanity.com/paper/2011.01844/) (2020)
- [Semantic Object Accuracy for Generative Text-to-Image Synthesis](https://mlsanity.com/paper/1910.13321/) (2019)
- [Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization](https://mlsanity.com/paper/1805.10966/) (2018)
- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [Survey of state-of-the-art mixed data clustering algorithms](https://mlsanity.com/paper/1811.04364/) (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)
- [Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning](https://mlsanity.com/paper/1811.08318/) (2018)
- [Semantic Clustering based Deduction Learning for Image Recognition and Classification](https://mlsanity.com/paper/2112.13165/) (2021)
- [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)
- [Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning](https://mlsanity.com/paper/2201.10713/) (2022)
- [Incremental cluster validity index-guided online learning for performance and robustness to presentation order](https://mlsanity.com/paper/2108.07743/) (2021)

## Built on by (most cited first, 4 of 4)

- [Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory](https://mlsanity.com/paper/2309.03487/) (2023)
- [An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter](https://mlsanity.com/paper/2511.17983/) (2025)
- [PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering](https://mlsanity.com/paper/2605.08673/) (2026)
- [Uncertainty-Aware Continual Learning for Open-World Intent Discovery Under an evolving Label Space](https://mlsanity.com/paper/2609.17866/) (2026)

## Abstract (arXiv)

In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose an ART-based topological clustering algorithm that integrates parameter estimation methods for both the similarity threshold and the edge deletion threshold. The similarity threshold is estimated using a determinantal point process-based criterion, while the edge deletion threshold is defined based on the age of edges. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art clustering algorithms without requiring parameter specifications specific to the datasets. Source code is available at https://github.com/Masuyama-lab/CAE