This paper presents a probabilistic unsupervised neural method in order to construct the lithofacies of the wells HM2 and HM3 situated in the south of Algeria (Sahara). Our objective is to facilitate the experts' work in geological domain and to allow them to obtain the structure and the nature of lands around the drilling quickly. For this, we propose the use of the Self-Organized Map (SOM) of...

In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that ...

We introduce a method for deriving a metric, locally based on the Fisher information matrix, into the data space. A self-organizing map (SOM) is computed in the new metric to explore financial statements of enterprises. The metric measures local distances in terms of changes in the distribution of an auxiliary random variable that reflects what is important in the data. In this paper the variab...

International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713722504 Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map Y. Hong ab; Y. -M. Chiang c; Y. Liu d; K. -L. Hsu a; S. Sorooshian a a Center for Hydrometeorology...

WEBSOM is a method in which the self-organizing map algorithm is used to automatically organize collections of documents on a map to enable easy exploration of the collection. This article illustrates with case studies how collections of various types of text can be successfully organized using the WEBSOM. The emphasis is on describing the particular challenges that each type of material poses,...

The self-organizing map is one of the most prominent unsupervised learning architectures used to visualize the similarities of high-dimensional input structures. What remains by no means straightforward , is an explicit representation of cluster boundaries in the nal two-dimensional map display. The detection of these boundaries rather requires some amount of insight into the inherent structure...