The self-organizing map has shown to be a stable neural network model for high-dimensional data analysis. However, its applicability is limited by the fact that some knowledge about the data is required to de ne the size of the network. In this paper we present the Growing Hierarchical SOM. This dynamically growing architecture evolves into a hierarchical structure of self-organizing maps accor...

In this paper we present an unsupervised text classification method based on the use of a self organizing map (SOM). A corpus of roughly 200 plain text documents have been considered. Some Scilab scripts have been prepared to read and process these documents, train the neural network and graphically render the

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-e...

A modification of the well-known PicSOM retrieval system is presented. The algorithm is based on a variant of the self-organizing map algorithm that uses bootstrapping. In bootstrapping the feature space is randomly sampled and a series of subsets are created that are used during the training phase of the SOM algorithm. Afterwards, the resulting SOM networks are merged into one single network w...

during an 11 days mission in february 2000 the shuttle radar topography mission (srtm) collected data over 80% of the earth's land surface, for all areas between 60 degrees n and 56 degrees s latitude. since srtm data became available, many studies utilized them for application in topography and morphometric landscape analysis. exploiting srtm data for recognition and extraction of topographic ...

Graphs as data structures are used in many applications, for example image analysis, scene description, natural language processing. The paper deals with Acyclic Graph Data Structures (AGDS) and with a learning process in a model of a Self-Organizing Map (SOM) that has been modified for processing of AGDS. To the modified SOM Neural Network (SOM NN), there are added contexts and counters which ...

As mentioned in Chapter 2, self-organizing maps (SOM) are a class of unsupervised neural networks whose characteristic feature is their ability to map nonlinear relations in multi-dimensional datasets into easily visualizable two-dimensional grids of neurons. SOM’s are also referred to as self-organized topological feature maps since the basic function of a SOM is to display the topology of a d...

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships wit...

Abstract The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we map regions with relative content rare process ( H → WW *). Second obtain Monte Carlo normalization factors different physics processes by fitting the dimensionally reduced repr...

In the paper, we discuss the visualization of multidimensional vectors taking into account the learning flow of the self-organizing neural network. A new algorithm realizing a combination of the self-organizing map (SOM) and Sammon’s mapping has been proposed. It takes into account the intermediate learning results of the SOM. The experiments have showed that the algorithm gives lower mean proj...