Strong geomagnetic activity is a hazard to electronics and electric power facilities. Assessment of the actual geomagnetic activity level from local magnetometer monitoring therefore is of importance for risk assessment but also in earth sciences and exploration. Wavelet based signal processing methods are applied to extract meaningful information from magnetic field time series in a noisy envi...

This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with differ...

The magnification behaviour of a generalized family of self-organizing feature maps, the Winner Relaxing and Winner Enhancing Kohonen algorithms is analyzed by the magnification law in the one-dimensional case, which can be obtained analytically. The Winner-Enhancing case allows to acheive a magnification exponent of one and therefore provides optimal mapping in the sense of information theory....

Finding structures in vast multidimensional data sets be they measurement data statistics or textual documents is di cult and time consuming Interesting novel relations between the data items may be hidden in the data The self organizing map SOM algorithm of Kohonen can be used to aid the exploration the structures in the data sets can be illustrated on special map displays In this work the met...

This work investigates pattern recognition techniques that can be used to identify the author of fine art paintings. The methodology proposed consists of four sequential phases: image acquisition, segmentation, feature extraction, and classification. Brushstrokes are segmented assuming that hue is uniform within a single brushstroke. Using a wavelet-based technique, textures are measured and us...

In this paper we present a method for document organization and retrieval based on statistical language modeling.The proposed method, which is based on the vector model, uses nonlinear interpolation to provide more accurate statistical estimators of the conditional probabilities employed for encoding the context of each word. An information retrieval system is built using the self-organizing ma...

We introduce a new method to improve web site text content by identifying the most relevant free text in the web pages. In order to understand the variations in web page text, we collect pages during a period. The page text content is then transformed into a feature vector and is used as input of a clustering algorithm (SOFM), which groups the vectors by common text content. In each cluster, a ...

In this paper we aim to detect some aspects of adjectival meanings. Concepts of adjectives are distributed by SOM (SelfOrganizing map) whose feature vectors are calculated by MI (Mutual Information). For the SOM obtained, we make tight clusters from map nodes, calculated by cosine. In addition, the number of tight clusters obtained by cosine was increased using map nodes and Japanese thesaurus....

Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achi...

Recent advancements in computing technology allowed both scientific and business applications to produce large datasets with increasing complexity and dimensionality. Clustering algorithms are useful in analyzing these large datasets but often fall short to provide completely satisfactory results. Integrating clustering and visualization not only yields better clustering results but also leads ...