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

• URL: http://arxiv.org/abs/2511.17983v3
• Date: Thu, 27 Nov 2025 17:08:37 GMT
• Title: An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
• Authors: Naoki Masuyama, Yuichiro Toda, Yusuke Nojima, Hisao Ishibuchi,
• Abstract summary: This paper proposes an Adaptive Resonance Theory (ART)-based topological clustering algorithm.<n>It autonomously adjusts its recalculation interval and vigilance threshold through a diversity-driven adaptation mechanism.<n> 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.
• Score: 6.868495647132414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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

Related papers

• NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data [3.4960553691816005]
  We present textNeuralFLoC, a fully unsupervised, end-to-end deep learning framework for joint functional registration and clustering.<n>Experiments on functional benchmarks show state-of-the-art performance in both registration and clustering, with robustness to missing data, irregular sampling, and noise, while maintaining scalability.
  arXiv  Detail & Related papers  (2026-02-03T06:35:33Z)
• Self-supervised Latent Space Optimization with Nebula Variational Coding [87.20343320266215]
  This paper proposes a variational inference model which leads to a clustered embedding.<n>We introduce additional variables in the latent space, called textbfnebula anchors, that guide the latent variables to form clusters during training.<n>Since each latent feature can be labeled with the closest anchor, we also propose to apply metric learning in a self-supervised way to make the separation between clusters more explicit.
  arXiv  Detail & Related papers  (2025-06-02T08:13:32Z)
• Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory [8.312275539092466]
  Iterative Refinement Adaptive Resonance Theory (IR-ART) integrates three key phases into a unified iterative framework.<n> IR-ART improves tolerance to suboptimal vigilance parameter values while preserving the parameter simplicity of Fuzzy ART.<n>Case studies visually confirm the algorithm's self-optimization capability through iterative refinement.
  arXiv  Detail & Related papers  (2025-05-07T14:12:39Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression [63.56922682378755]
  We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform. Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
  arXiv  Detail & Related papers  (2023-08-17T01:34:51Z)
• A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning [20.995946115633963]
  We propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications.
  arXiv  Detail & Related papers  (2023-05-01T01:04:07Z)
• Streaming Inference for Infinite Non-Stationary Clustering [9.84413545378636]
  Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, streaming, non-stationary) in the context of clustering, also known as mixture modeling. We introduce a novel clustering algorithm that endows mixture models with the ability to create new clusters online, as demanded by the data.
  arXiv  Detail & Related papers  (2022-05-02T21:05:18Z)
• Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning [8.581682204722894]
  This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from a distribution of data points. For the improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed.
  arXiv  Detail & Related papers  (2022-01-26T02:34:52Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.