The paper sheds light on the use of a self-learning GNG neural network for identification and
exploration of the purchasing behaviour patterns. The test has been conducted on the data …

Spectral clustering (SC) is one of the most popular clustering methods and often outperforms
traditional clustering methods. SC uses the eigenvectors of a Laplacian matrix calculated …

Abstract Self-Organizing Neural Networks (SONNs) have a wide range of applications with
massive computational requirements that often need to be satisfied with optimized parallel …

Travel time of vehicles in urban traffic networks can be reduced by using Adaptive Traffic
Signal Control (ATSC) to change the signal program according to the current traffic situation …

The growing neural gas is a well-known neural network model in unsupervised learning
missions, such as vector quantization and clustering. With the rampant growth of data and …

Abstract Growing Neural Gas (GNG) constitutes a neural network algorithm to create
topology preserving representations of data, thus, being applicable in cluster analysis. With …

The paper deals with the high dimensional data clustering problem. One possible way to
cluster this kind of data is based on Artificial Neural Networks (ANN) such as Growing …

The twenty-first century has been a time of significant changes in the lives of many societies
worldwide. The constantly increasing access to the web has changed many aspects of …

The research described in this thesis was motivated by the need of a robust model capable
of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition …

The size, complexity and dimensionality of data collections are ever increasing from the
beginning of the computer era. Clustering is used to reveal structures and to reduce large …