It has been shown earlier that the Self-Organizing Map (SOM) can be applied to the analysis and visualization of similarities of words in their usage in short contexts formed of adjacent words. These SOMs of words, called word category maps, have many potential applications. One of the application areas is information retrieval and data mining of textual document collections where the word cate...

The availability of linguistic corpora in electronic form has made it possible to make various kinds of computer-based analyses on them. The use of Self-Organizing Map (SOM) in the analysis and visualization of various aspects of textual material is outlined. The basic approach for creating maps of lexical items, documents, or set of languages or dialects is described with reference to original...

Assessment of model properties with respect to data is important for reliable analysis of data. After training, Self-Organizing Map (SOM) can be assessed, for instance, with respect to its quantization or its topology preservation properties with onenumber summaries. In this paper, we present a decomposition of the SOM distortion measure for measuring different aspects of the SOM for map units ...

In this paper we present a novel system that can automatically mark up text documents into XML. The system uses the Self-Organizing Map (SOM) algorithm to organize marked documents on a map so that similar documents are placed on nearby locations. Then by using the inductive learning algorithm C5, it automatically generates and applies the markup rules from the nearest SOM neighbours of an unma...

In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In ...

In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Evolving Fuzzy Case-Based Reasoning (EFCBR) for flow time prediction is proposed. Genetic Algorithms (GAs) is applied to fine-tune the fuzzy term numbers and weights of fuzzy features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, correspondi...

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, as the original input manifold can be complicated with an inherent dimension larger than that of the feature map, the dimension reduction in SOM can be too drastic, generating a folded feature map. In order to eliminate this pheno...

We construct a benchmarking model in the form of a twodimensional self-organising map (SOM) to compare the performance of nonbanking financial institutions (NFIs) in Romania. The NFIs are characterized by a number of performance dimensions such as capital adequacy, assets’ quality and profitability. First, we apply Kohonen’ SOM algorithm (an unsupervised neural network algorithm) to group the N...

Kohonen’s self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks ...