  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.
