ART-based Modular Networks for Incremental Learning of Hierarchical Clusterings

This paper introduces HART-S, a new modular neural network that can incrementally learn stable hierarchical clusterings of arbitrary sequences of input patterns by self-organisation. The network is a cascade of Adaptive Resonance Theory (ART) modules, in which each module learns to cluster the differences between the input pattern and the selected category prototype at the previous module. Input patterns are first classified into a few broad categories, and successive ART modules find increasingly specific categories until a threshold is reached, the level of which can be controlled by a global parameter called "resolution". The network thus essentially implements a divisive (or splitting) hierarchical clustering algorithm: hence the name HART-S (for "Hierarchical ART with Splitting")