Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the K...

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/ vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF w...

this paper proposes a two-step approach for characterizing the reservoir properties of the world’s largest non-associated gas reservoir. this approach integrates geological and petrophysical data and compares them with the field performance analysis to achieve a practical electrofacies clustering. porosity and permeability prediction is done on the basis of linear functions, succeeding the elec...

Self-Organizing Maps (SOMs) are widely used in engineering and data-analysis tasks, but so far rarely in very large-scale problems. The reason is the amount of computation: while small SOMs can be computed starting from the basic principles, rapid computation of large maps of high-dimensional data requires special methods. Winner search, nding the position of a data sample on the map, is the co...

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 chapter introduces the use of high-resolution self-organizing maps (SOM) to represent a large number of geographic features on the basis of their attributes. Until now, the SOM method has been applied to geographic data for both clustering and visualization purposes. However, the granularity of the resulting attribute space representations has been far below the resolution at which geograp...

An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achie...

Kohonen's Self-Organizing Map (SOM) is one of the most popular arti cial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a prior...

MOTIVATION With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector mac...

We present an alternative algorithm to global fitting procedures to construct Par-ton Distribution Functions parametrizations. The proposed algorithm uses Self-Organizing Maps which at variance with the standard Neural Networks, are based on competitive-learning. Self-Organizing Maps generate a non-uniform projection from a high dimensional data space onto a low dimensional one (usually 1 or 2 ...