Automatic Vigilance Parameter Estimation for Adaptive Resonance Theory-based Topological Clustering

Abstract

Thanks to a continual learning capability and superior classification performance, Adaptive Resonance Theory (ART)-based clustering has been actively studied. Especially, ART-based topological clustering algorithms have shown superior clustering performance than other clustering algorithms. However, those algorithms have a data dependent parameter, called a vigilance parameter, which has a significant impact on the clustering performance. This paper introduces an automatic vigilance parameter estimation method to an ART-based topological clustering algorithm. Experimental results show that the proposed algorithm achieves superior clustering performance on a 2D synthetic dataset and real-world datasets compared to conventional algorithms.