This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …

Exploratory data analysis (EDA) using data clustering is extremely important for
understanding the basic characteristics of a novel data set before developing complex …

This article presents an adaptive resonance theory predictive mapping (ARTMAP) model,
which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning …

Validation is one of the most important aspects of clustering, particularly when the user is
designing a trustworthy or explainable system. However, most clustering validation …

Biclustering is a powerful tool for exploratory data analysis in domains such as social
networking, data reduction, and differential gene expression studies. Topological learning …

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

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

In streaming data applications, the incoming samples are processed and discarded, and
therefore, intelligent decision-making is crucial for the performance of lifelong learning …

Biclustering is a special case of subspace clustering that has become viable in several
domains. Particularly, in genomic data analysis, biclustering has been used to identify …

Deep neural networks (or DNNs) are frequently used for their accuracy and adaptive nature
in the research field of automatic classification tasks. State-of-the-art architectures and …