We discuss Bregman divergences and the very close relationship between a class of these divergences and the regular family of exponential distributions before applying them to various topology preserving dimension reducing algorithms. We apply these methods to identification of structure in magnetic resonance images of the brain and show that different divergences reveal different structure in ...

Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be used for clustering, visualisation, and dimensionality reduction. However for visualisation purposes the SOM uses colouring schemes as a means of marking cluster boundaries on the map. The distribution of the data and t...

This paper focuses on the use of self-organising maps, also known as Kohonen maps, for the classification task of text documents. The aim is to effectively and automatically classify documents to separate classes based on their topics. The classification with self-organising map was tested with three data sets and the results were then compared to those of six well known baseline methods: k-mea...

This paper introduces a new concept to the processing of graph structured information using self organising map framework. Previous approaches to this problem were limited to the processing of bounded graphs. The computational complexity of such methods grows rapidly with the level of connectivity, and are restricted to the processing of positional graphs. The concept proposed in this paper add...

Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Here...

Purpose Neural document clustering techniques, e.g., self-organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity. However, the quantity of text is ever-increasing. We propose a novel dynamic adaptive self-organising hybrid (DASH) model, which adapts to time-event news collections not only to the neural topological structure but al...

A text clustering neural model, traditionally, is assumed to cluster static text information and represent its inner structure on a flat map. However, the quantity of text information is continuously growing and the relationships between them are usually complicated. Therefore, the information is not static and a flat map may be not enough to describe the relationships of input data. In this pa...

The paper deals with the analysis of Research and Technology Development (RTD) in the Central European countries and the relation of RTD with economic and social parameters of countries in this region. A methodology has been developed for quantitative and qualitative ranking and estimates of relationship among multidimensional objects on the base of such analysis. The knowledge has been discove...