The Self-Organizing Map (SOM ) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper we describe Fast Learning SOM (FLSOM ) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that ...

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 ...

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are imp...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In the Entire project, a data mining tool using the SOM was implemented and used to analyse world pulp and paper technology.

Both the Self-Organizing Map (SOM) and fuzzy ARTMAP neural network are trained based upon the competitive mechanism and use the “winner-take-all” rule. Previous studies developed soft classification algorithms for the SOM. This paper introduces the idea and proposes non-parametric measures for the fuzzy ARTMAP computational neural networks to handle spatial uncertainty in remotely sensed imager...

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...

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...

In this paper, we propose an unsupervised neural network for prediction and planning of complex robot trajectories. A general approach is developed which allows Kohonen's Self-Organizing Map (SOM) to approximate nonlinear input-output dynamical mappings for trajectory reproduction purposes. Tests are performed on a real PUMA 560 robot aiming to assess the computational characteristics of the me...

Spatial hypertext systems use physical properties as color, dimensions, and position to represent relationships between documents. These systems allows the user to express a lot of different relationships between information but the structure should be build by hand by the user. This can be complex if a large number of information is involved. Self-organizing neural networks map can automatical...

This paper presents a novel architecture of SOM which organizes itself over time. The proposed method called MIGSOM (Multilevel Interior Growing Self-Organizing Maps) which is generated by a growth process. However, the network is a rectangular structure which adds nodes from the boundary as well as the interior of the network. The interior nodes will be added in a superior level of the map. Co...