Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

• Published in: arXiv.org
• Main Authors: Masuyama, Naoki, Amako, Narito, Yamada, Yuna, Nojima, Yusuke, Ishibuchi, Hisao
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 07.07.2022
• Subjects: Algorithms; Cluster analysis; Clustering; Computer Science - Learning; Computer Science - Neural and Evolutionary Computing; Data points; Information retrieval; Machine learning; Resonance; Similarity; Structural hierarchy; Topology
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2201.10713

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.