Among the large number of research publications discussing the SOM (Self-Organizing Map) [1, 2, 18, 19] different variants and extensions have been introduced. One of the SOM based models is the Growing Hierarchical Self-Organizing Map (GHSOM) [3-6]. The GHSOM is a neural architecture combining the advantages of two principal extensions of the self-organizing map, dynamic growth and hierarchica...

The Self-Organizing Map (SOM) has shown to be a stable neural network model for highdimensional data analysis. However, its applicability is limited by the fact that some knowledge about the data is required to define the size of the network. In this paper the Growing Hierarchical SOM (GHSOM) is proposed. This dynamically growing architecture evolves into a hierarchical structure of self–organi...

In this paper, we study fundamental properties of the Self-Organizing Map (SOM) and the Generative Topographic Mapping (GTM), ramifications of the initialization of the algorithms and properties of the algorithms in the presence of missing data. We show that the commonly used principal component analysis (PCA) initialization of the GTM does not guarantee good learning results with high-dimensio...

We have experimented with a bio-inspired selforganizing texture and hardness perception system which automatically learns to associate the representations of the two submodalities with each other. To this end we have developed a microphone based texture sensor and a hardness sensor that measures the compression of the material at a constant pressure. The system is based on a novel variant of th...

In the paper, text mining and visualization by self-organizing map (SOM) are investigated. At first, textual information must be converted into numerical one. The results of text mining and visualization depend on the conversion. So, the influence of some control factors (the common word list and usage of the stemming algorithm) on text mining results, when a document dictionary is created, is ...

Wireless Sensor Networks (WSNs) consist of many sensor nodes, which are used for capturing the essential data from the environment and sending it to the Base Station (BS). Most of the research has been focused on energy challenges in WSN. There are many notable studies on minimization of energy consumption during the process of sensing the important data from the environment where nodes are dep...

Not linear methods for statistical data analysis have become more and more popular thanks to the rapid development of computers. The fields in which they are applied to are as various as the methods them self. Generative topographic mapping (GTM) has been developed by [Bishop et al. 1997] as a principal alternative to the self-organizing map (SOM) algorithm [Kohonen 1982] in which a set of unla...

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research. By using Self-Organizing Map (SOM) approaches as the engine, through the experiment, it is shown that SOM has the capability to detect the number of connection chains that involved in a stepping stones. Realizing that by counting the number of connection...

We investigate in this paper the problem of model collisions in the Dissimilarity Self Organizing Map (SOM). This extension of the SOM to dissimilarity data suffers from constraints imposed on the model representation, that lead to some strong map folding: several units share a common prototype. We propose in this paper an efficient way to address this problem via a branch and bound approach.