Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

Environmental measurements generate great volumes of high-dimensional data (often noisy
and with missing values) from which meaningful messages may be extracted through …

Abstract The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount
of interest among researches and practitioners in a wide variety of elds. The SOM and a …

For many years, artificial neural networks (ANNs) have been studied and used to model
information processing systems based on or inspired by biological neural structures. They …

In the study, we discussed the ARCH/GARCH family models and enhanced them with
artificial neural networks to evaluate the volatility of daily returns for 23.10. 1987–22.02 …

In exploratory analysis of high-dimensional data the self-organizing map can be used to
illustrate relations between the data items. We have developed two measures for comparing …

Background Conventionally, the first step in analyzing the large and high-dimensional data
sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering …

There is a long cartographic tradition of describing cities through a focus on the
characteristics of their residents. A review of the history of this type of urban social analysis …

The self-organizing map is used for analysis of financial statements, focusing on bankruptcy
prediction. The phenomenon of going bankrupt is analyzed qualitatively, and companies are …

This paper describes and compares three clustering techniques: traditional clustering
methods, Kohonen maps and latent class models. The paper also proposes some novel …