The clustering structures formed by Adaptive Resonance Theory (ART) and many other
algorithms are dependent on input presentation/permutation order. In this work, we exploit …

This paper presents a novel adaptive resonance theory (ART)-based modular architecture
for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA) …

The internal representation of the categories in Adaptive Resonance Theory (ART) neural
networks can greatly affect the quality and compactness of the discovered clusters. Dual …

Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to
their internal categorical representation. This study extends the capabilities of ART by …

One of the distinguishing features of Adaptive Resonance Theory (ART) is that it relies on a
second similarity check, called a vigilance test, to accept or reject a sample into a given …

Fuzzy Adaptive Resonance Theory (ART) is a classic unsupervised learning algorithm. Its
performance on a particular clustering problem is sensitive to the suitability of the category …

Clustering is an important tool in data mining and knowledge discovery. Fuzzy Adaptive
Resonance Theory, a member of unsupervised neural networks, clusters data effectively …

Validation is one of the most important aspects of clustering, but most approaches have
been batch methods. Recently, interest has grown in providing incremental alternatives. This …

Fuzzy ARTMAP (FAM) is a neural network model based on the adaptive resonance theory
(ART). Its main advantage relies on its capability to successfully deal with the stability …

Due to their online learning nature, incremental clustering techniques can handle a
continuous stream of data. In particular, various incremental clustering techniques based on …