An ART-based modular architecture for learning hierarchical clusterings

This paper introduces a neural architecture (HART for ''Hierarchical ART'') that is capable of learning hierarchical clusterings of arbitrary input sequences, The network is built up of layers of Adaptive Resonance Theory (ART) network modules where each layer learns to cluster the prototypes developed at the layer directly below it. The notion of effective vigilance is introduced to refer to the vigilance level of multiple ART modules in a HART network. An upper bound is derived for the number of HART layers needed in the case when all ART modules have the same vigilance. Experiments were carried out on a machine learning benchmark database to demonstrate the developed internal representation as well as some learning properties of two- and three-layer binary HART networks.