An important goal of knowledge discovery is the search for patterns in data that can help
explain the underlying process that generated the data. The patterns are required to be new …

Self-Organizing Maps have been applied in various industrial applications and have proven
to be a valuable data mining tool. In order to fully benefit from their potential, advanced …

A high-quality distance preserving output representation is provided to the neural gas (NG)
network. The nonlinear mapping is determined concurrently along with the codebook …

The evolution of wireless network technologies and the recent advances in mobile
computing hardware have made possible the introduction of various applications in mobile …

The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is
very useful for processing data of high dimensionality and complexity. Visualization methods …

The training of Emergent Self-organizing Maps (ESOM) with large datasets can be a
computationally demanding task. Batch learning may be used to speed up training. It is …

Information visualization and visual data mining leverage the human visual system to
provide insight and understanding of unorganized data. In order to scale to massive sets of …

The visualization of distance structures in high dimensional data as topographic maps (U-
matrix) is a standard method for Emergent Self Organizing Maps (ESOM). This work …

U* C is a recently proposed clustering algorithm using Emergent Self-Organizing Maps
(ESOM). U* C clustering is superior to standard clustering algorithms such as K-means and …

In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs)
based on Kohonen Self–Organizing maps in order to detect intrusive behaviours. The …