Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory

TL;DR

This paper introduces IR-ART, an iterative framework that enhances Fuzzy ART's robustness to vigilance parameter settings, enabling more reliable clustering without complex hyperparameter tuning.

Contribution

IR-ART integrates cluster stability detection, pruning, and vigilance expansion into a unified iterative process, improving robustness and usability of Fuzzy ART.

Findings

IR-ART outperforms traditional Fuzzy ART on 15 datasets.

It maintains simplicity while improving tolerance to vigilance parameter variations.

Case studies demonstrate effective self-optimization through iteration.

Abstract

The clustering performance of Fuzzy Adaptive Resonance Theory (Fuzzy ART) is highly dependent on the preset vigilance parameter, where deviations in its value can lead to significant fluctuations in clustering results, severely limiting its practicality for non-expert users. Existing approaches generally enhance vigilance parameter robustness through adaptive mechanisms such as particle swarm optimization and fuzzy logic rules. However, they often introduce additional hyperparameters or complex frameworks that contradict the original simplicity of the algorithm. To address this, we propose Iterative Refinement Adaptive Resonance Theory (IR-ART), which integrates three key phases into a unified iterative framework: (1) Cluster Stability Detection: A dynamic stability detection module that identifies unstable clusters by analyzing the change of sample size (number of samples in the cluster) in iteration. (2) Unstable Cluster Deletion: An evolutionary pruning module that eliminates low-quality clusters. (3) Vigilance Region Expansion: A vigilance region expansion mechanism that adaptively adjusts similarity thresholds. Independent of the specific execution of clustering, these three phases sequentially focus on analyzing the implicit knowledge within the iterative process, adjusting weights and vigilance parameters, thereby laying a foundation for the next iteration. Experimental evaluation on 15 datasets demonstrates that IR-ART improves tolerance to suboptimal vigilance parameter values while preserving the parameter simplicity of Fuzzy ART. Case studies visually confirm the algorithm's self-optimization capability through iterative refinement, making it particularly suitable for non-expert users in resource-constrained scenarios.