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 ...

We present a novel system for automatically marking up text documents into XML and discuss the benefits of XML markup for intelligent information retrieval. The system uses the Self-Organizing Map (SOM) algorithm to arrange XML marked-up documents on a twodimensional map so that similar documents appear closer to each other. It then employs an inductive learning algorithm C5 to automatically ex...

A method for discrimination and classification of breast cancer dataset with benign and malignant tissues is proposed using Independent Component Analysis (ICA) and Self Organizing Map (SOM). The method implement ICA for preprocessing and data reduction and SOM for data analysis. The best performance was obtained with ICASOM, resulting in 98.8% classification accuracy and a SOM result is 94.9%.

In this paper we test the Self-Organizing Map (SOM) on the problem of predicting chaotic time-series (speciically Mackey-Glass series) with local linear models deened separately for each of the prototype vectors of the SOM. We see that the method achieves good results. This together with the capabilities of the SOM make it a valuable tool in exploratory data mining.

In this paper we test the Self-Organizing Map (SOM) on the problem of predicting chaotic time-series (speci cally Mackey-Glass series) with local linear models de ned separately for each of the prototype vectors of the SOM. We see that the method achieves good results. This together with the capabilities of the SOM make it a valuable tool in exploratory data mining.

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...

We propose a two-stage hierarchical arti®cial neural network for the segmentation of color images based on the Kohonen self-organizing map (SOM). The ®rst stage of the network employs a ®xed-size two-dimensional feature map that captures the dominant colors of an image in an unsupervised mode. The second stage combines a variable-sized one-dimensional feature map and color merging to control th...

The Self-Organizing Map (SOM) is a useful and strong tool for data analysis, especially for large data sets or data sets of high dimensionality. SOM visualizations map the data model dimensions to visual dimensions like color and position, thus they help exploring the SOM. Visualization can also involve the data itself so that it helps accessing information that are not available in the trained...

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...

Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the K...