The Kohonen self-organization map is usually considered as a classification or clustering tool, with only a few applications in time series prediction. In this paper, a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting ...

Kohonen maps are self-organizing neural networks that classify and quantify n-dimensional data into a one- or two-dimensional array of neurons. Most applications of Kohonen maps use simulations on conventional computers, eventually coupled to hardware accelerators or dedicated neural computers. The small number of different operations involved in the combined learning and classification process...

We show how it is possible to use the Kohonen self-organizing algorithm to deal with data with missing values and estimate them. After a methodological reminder, we illustrate our purpose with three applications to real-world data. Nous montrons comment il est possible d’utiliser l’algorithme d’autoorganisation de Kohonen pour traiter des données avec valeurs manquantes et estimer ces dernières...

The Kohonen algorithm (SOM, Kohonen 1995) is a very powerful tool for data analysis. Most of the time, each observation is a p-vector of numerical values. But in many cases, for survey analysis for example, the observations are described by qualitative variables with a finite number of modalities. In that case, we define a specific algorithm (KDISJ) which provides a simultaneous classification ...

Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power, and generalization ability. The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering...

NETWORK FOR VOLUMETRIC MEASURMENTS ON BRAIN CT IMAGING Mohamed N. Ahmed, and Aly A. Farag Computer Vision and Image Processing Lab University of Louisville, Department of Electrical Engineering Louisville, KY 40292 E-mail:[email protected], Phone:(502)-852-7510, Fax:(502)852-6807 Abstract|In this paper, we present a new system to segment and label CT Brain slices using a self-organ...

It is well known that the SOM algorithm achieves a clustering of data which can be interpreted as an extension of Principal Component Analysis, because of its topology-preserving property. But the SOM algorithm can only process real-valued data. In previous papers, we have proposed several methods based on the SOM algorithm to analyze categorical data, which is the case in survey data. In this ...

The question of self-organization for the Kohonen algorithm is investigated. First the notions of organized states, weak and strong self-organizations are precisely defined. Then, combining mathematical and simulation results we prove that the Kohonen algorithm has not the strong self-organization property at least in two well-known cases: the stimuli space is [0, 1](2), the unit set is a line ...

The Kohonen algorithm has very interesting properties of self organization, which are widely used for exploratory data analysis and visualization. But the Kohonen maps can also be useful to forecasting tasks, study of temporal evolutions, explanation of complex prediction models. The examples that are used to present the methods are issued from several papers by Patrice Gaubert, Bernard Girard,...