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...

The Kohonen Self Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Batch SOM algorithms based on adaptive and non-adaptive city-blo...

This paper presents a generalized framework of a self-organizing map (SOM) applicable to more extended data classes rather than vector data. A modular structure is adopted to realize such generalization; thus, it is called a modular network SOM (mnSOM), in which each reference vector unit of a conventional SOM is replaced by a functional module. Since users can choose the functional module from...

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, t...

The Kohonen self-organizing map (SOM) is an unsupervised neural network with a competitive learning strategy that uses a neighborhood lateral interaction function to discover the hidden topological structure of the input data and has both visualization and clustering properties. In this presentation, we propose batch SOM algorithms with automatic weighting of the variables to training the Kohon...

Self-Organizing Map is an unsupervised neural network which combines vector quantization and vector projection. This makes it a powerful visualization tool. SOM Toolbox implements the SOM in the Matlab 5 computing environment. In this paper, computational complexity of SOM and the applicability of the Toolbox are investigated. It is seen that the Toolbox is easily applicable to small data sets ...

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the tw...

This paper reviews the principles and several different applications of the LAMSTAR (Large Memory Storage and Retrieval) Neural Network. The LAMSTAR was specifically developed for application to problems involving very large memory that relates to many different categories (attributes), where some of the data is exact while other data are fuzzy and where, for a given problem, some data categori...

Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VA...

A new method to generate coarse meshes for overlapping unstructured multigrid algorithm based on self-organizing map (SOM) neural network is presented in this paper. The application of SOM neural network can overcome some limitations of conventional methods and which is designed to pursuit the best structure relation between fine and coarse unstructured meshes with the object to ensure robust c...