The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric relationships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements an...

Prototype-based clustering algorithms such as the Self Organizing Map (SOM) or Neural Gas (NG) offer powerful tools for automated data inspection. The distribution of prototypes, however, does not coincide with the underlying data distribution and magnification control is necessary to obtain information theoretic optimum maps. Recently, several extensions of SOM and NG to general non-vectorial ...

The Self-Organizing Map (SOM) is a powerful neural network for analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional input data into simple geometric relationships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inh...

The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to each other. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection. This article presents research in which we sought to validate this property of SOM, called the Pro...

The nature inspired approaches represent a new trend in computer science in general and in the Semantic Web, due to their scalability and robustness. Neural networks represent one category of nature inspired solutions. The self-organizing map (SOM) is a very popular unsupervised neural network model (Kohonen, et al., 2000). It is a data mining and visualization method for complex high dimension...

Self-organizing map (SOM) is one of the most popular neural network methods for cluster analysis. Clustering methods using SOM usually are two-stage procedures: first original data are projected onto a set of prototypes on an ordered grid by SOM, and these prototypes can be seen as proto-clusters which will be grouped in the second stage to obtain finally clustering results. Many methods have b...

Every Organizations need to understand their customer’s behavior, preferences and future needs, which depend on past behavior. Web Usage Mining is an active research topic in which user session clustering is done to understand user’s activities. In this paper, we use Neural based approach Self Organizing Map for clustering of session as a trend analysis with some parameters. It depends on the p...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional input data into simple geometric relationships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and...

We propose a new method called C-SOM for function approximation. C-SOM extends the standard Self-Organizing Map (SOM) with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve standard SOMs' generalization capabilities. CSOM uses the gradient information provided by the LLM technique to compute a cubic spline interpolation in the input space bet...

permeability can be directly measured using cores taken from the reservoir in the laboratory. due to high cost associated with coring, cores are available in a limited number of wells in a field. many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. the main obj...