In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that ...

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. They represent powerful data analysis tools applied in many different areas including areas such as biomedicine, bioinformatics, proteomics, and astrophysics. We maintain a data analysis package in R based on self-organizing maps. The package supports efficient, statistical measures that enable the u...

The self-organizing map (SOM) is a method that represents statistical data sets in an ordered fashion, as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed. As a case study that instructs how to use the SOM to compare states of economic systems, the standard of living of different countries is analyzed using the SOM. Based on a...

information from multidimensional primary signals, and to represent it as a location, say, in a two-dimensional network. Although this i s already a step towards generalization and symbolism, it must be admitted that the extraction of features from geometrically or physically relatable data elements i s still a very concrete task, in principle at least. Theoperation of the brain at the higher l...

We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative SelfOrganizing Map (A-SOM). The A-SOM is similar to the SOM and thus develops a representation of its input space, but in addition it also learns to associate its activity with the activity of one or several external SOMs. The A-SOM has relevance in e.g. the modelling of expectations in one modality du...

Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the “border effect”. In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce a 2D rectangular grid data structure for r...

It has been proved that in one-dimensional cases, the weights of Kohonen’s self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difŽcult to analyze Kohonen’s SOMs in multidimensional cases; however, it has been conjectured that similar results seem to be obtainable in multidimensional cases. In t...