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

• URL: http://arxiv.org/abs/2201.10713v1
• Date: Wed, 26 Jan 2022 02:34:52 GMT
• Title: Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning
• Authors: Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi
• Abstract summary: 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.
• Score: 8.581682204722894
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Thanks to an ability for handling the plasticity-stability dilemma, Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning. In general, however, the clustering performance of ART-based algorithms strongly depends on 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 a distribution of data points. In addition, for the improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Simulation experiments show that the proposed algorithm shows the comparative clustering performance compared with recently proposed hierarchical clustering algorithms.

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