  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.
