Hierarchical clustering is an important and powerful but computationally extensive
operation. Its complexity motivates the exploration of highly parallel approaches such as …

For pt. I see ibid., p. 645-61 (2002). Part I of this paper defines the class of constructive
unsupervised on-line learning simplified adaptive resonance theory (SART) clustering …

This paper introduces a modular neuro-fuzzy architecture (FUZZY HART-S) that is capable
of incremental learning of stable hierarchical clusterings of arbitrary sequences of analogue …

Abstract Fuzzy ART and Fuzzy ARTMAP are two prominent neural network architectures
based on the principles of Grossberg's Adaptive Resonance Theory (ART). While the former …

The adaptive resonance theory 2 (ART2) neural network exhibits several properties which
can be useful in the data mining and which are lacking in most other neural networks. But …

Part I of this paper proposes a definition of the adaptive resonance theory (ART) class of
constructive unsupervised on-line learning clustering networks. Class ART generalizes …

The paper compares two modular neural network architectures, built up of adaptive
resonance theory (ART) networks, that can develop stable two-level hierarchical clusterings …

An integrated adaptive fuzzy clustering (IAFC) algorithm using a structure similar to that
found in the Adaptive Resonance Theory (ART-1) neural network, is presented. The IAFC …

Unsupervised neural networks are known for their ability to cluster inputs into categories
based on the similarity among inputs. Fuzzy Adaptive Resonance Theory (Fuzzy ART) is a …

Adaptive resonance theory (ART) is a neural-network based clustering method developed
by GA Carpenter and S. Grossberg (1987). Its inspiration is neurobiological and its …