Improved data visualization will be a significant tool to enhance cluster analysis. In this
paper, an information-theoretic-based method for cluster visualization using self-organizing …

A powerful method in the analysis of datasets where there are many natural clusters with
varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the …

In this paper we elaborate on the challenges of learning manifolds that have many relevant
clusters, and where the clusters can have widely varying statistics. We call such data …

With all the exciting advances in sensor fusion and data interpretation technologies in recent
years, including co-registration, 3-D surface reconstruction, object recognition, spatial …

This paper presents a new neural method for unsupervised learning denoted as Magnitude
Sensitive Competitive Learning (MSCL), which has the property of distributing the unit …

Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for
correct clustering. Among several existing measures of topology violation, this paper studies …

Cluster analysis can be broadly divided into multivariate data visualization, clustering
algorithms, and cluster validation. This dissertation contributes neural network-based …

In the last decades several analytical techniques have been used in archaeological
ceramics studies. However, instrumental neutron activation analysis, INAA, employing …

Finding critical information in large and complex data volumes is a challenge increasingly
posed by real systems such as NASA's space and Earth science missions. Many of these …

V diplomskem delu bomo predstavili Kohonenovo nevronsko mrežo in preučili njeno
strukturo, učenje z algoritmom SOM, parametre in uporabo predvsem pri klasifikaciji …