Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better per...

For a solution of the visual correspondence problem we have modified the Self Organizing Map (SOM) to map image planes onto another in a neighborhoodand feature-preserving way. We have investigated the convergence speed of this SOM and Dynamic Link Matching (DLM) on a benchmark problem for the solution of which both algorithms are good candidates. We show that even after careful parameter adjus...

In order to remove the “border effect”, several spherical Self-Organizing Maps (SOM) based on the geodesic dome have been proposed. However, existing neighborhood searching methods on the geodesic dome are much more time-consuming than searching on the normal rectangular/hexagonal grid. In this paper, we present detailed descriptions of the algorithms used in training the Geodesic SOM (GeoSOM),...

Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the proc...

A Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional complex data on a two-dimensional map. We modified the conventional SOM to genome informatics, making the learning process and resulting map independent of the order of data input, and developed a novel bioinformatics tool for phylogenetic classification of sequence fragments obtained from pooled ge...

The Self-Organizing Map (SOM) is a widely used data visualization tool in engineering applications. The algorithm performs a non-linear mapping from a highdimensional data space to a low-dimensional space, which is typically a two-dimensional, rectangular grid. This makes it possible to present multidimensional data in two dimensions. Often the model vectors of the SOM and a new data sample nee...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A major drawback of the SOM has been the lack of a theoretically justified criterion for model selection. Model complexity has a decisive effect on the reliability of visual data analysis, which is a main application of the SOM. In particular, independence of variables cannot be observed unless generalization of t...

In this paper the "Self-Organizing (Feature) Map" (SOM) methodology as originally proposed by Kohonen (1982) is employed in the context of Competitive Market Structure (CMS) and segmentation analysis using household-speci c brands preferences derived from diary panel data as input patterns for SOM training. The adaptive SOM algorithm results in a representation of competitive structures among r...

Several methods to visualize clusters in high-dimensional data sets using the Self-Organizing Map (SOM) have been proposed. However, most of these methods only focus on the information extracted from the model vectors of the SOM. This paper introduces a novel method to visualize the clusters of a SOM based on smoothed data histograms. The method is illustrated using a simple 2-dimensional data ...

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 dif ferent countries is analyzed using the SOM Based on a grea...