The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM w...

Unsupervised learning scheme like the self-organizing map (SOM) has been used to classify speech sounds in an ordered manner. SOM is able to extract the most salient features of the input signal and provides a simple way of visualizing them. The distance between two units on the map was used as an objective measure of their perceptual similarity. This paper presents a study of the evaluation of...

The Kohonen Self Organizing Map (SOM), is a topology preserving map that maps data from higher dimensions onto a (typically) two dimensional grid of lattice points[3]. The aim of Self-Organization is to generate a topology preserving mapping, where the neighborhood relations in the input space are preserved as well as possible, in the neighborhood relations of the units of the map[2]. One of th...

The application of a hierarchical self{organizing map (HSOM) to the problem of segmentation of multispectral magnetic resonance (MR) images is investigated. The HSOM is composed of several layers of the self{organizing map (SOM) organized in a pyramidal fashion. The SOM has been used for the segmentation of multispectral MR images but the results often su er from undersegmentation and oversegme...

To keep an online game interesting to its users, it is important to know them. In this paper, in order to characterize user characteristics, we discuss clustering of online-game users based on their trails using Self Organization Map (SOM). As inputs to SOM, we introduce transition probabilities between landmarks in the targeted game map. An experiment is conducted confirming the effectiveness ...

The S-Map is a network with a simple learning algorithm that combines the self-organization capability of the Self-Organizing Map (SOM) and the probabilistic interpretability of the Generative Topographic Mapping (GTM). The simulations suggest that the SMap algorithm has a stronger tendency to self-organize from random initial configuration than the GTM. The S-Map algorithm can be further simpl...

The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generaliz...

This paper proposes an extension of an SOM called the “SOM of SOMs,” or SOM, in which objects to be mapped are self-organizing maps. In SOM, each nodal unit of a conventional SOM is replaced by a function module of SOM. Therefore, SOM can be regarded as a variation of a modular network SOM (mnSOM). Since each child SOM module in SOM is trained to represent an individual map, the parent map in S...

In this paper is developed an intelligent searching tool using the Self-Organizing Map (SOM) algorithm, as a prototype e-content retrieval tool. The proposed searching tool has the ability to adjust and scale into any e-learning platform that requires concept-based queries. The SOM algorithm has been used successfully for the document organization as well as for document retrieval. In the propo...

Multiscale structures and algorithms that unify the treatment of local and global scene information are of particular importance in image segmentation. Vector quantization, owing to its versatility, has proved to be an effective means of image segmentation. Although vector quantization can be achieved using self-organizing maps with competitive learning, self-organizing maps in their original s...