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

2023/05/01 by Masuyama, Naoki, Takebayashi, Takanori, Nojima, Yusuke +3 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.2305.01507

Abstract

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 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.

Cited by

• MLATC: Fast Hierarchical Topological Mapping from 3D LiDAR Point Clouds Based on Adaptive Resonance Theory
• An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter

Related

• An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
• Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory
• Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence
• Stable and consistent density-based clustering via multiparameter persistence
• AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm