In this paper a new tool for visualizing self organizing maps (SOM) for interdisciplinary explorative data analysis is proposed, the SOM reef. By combination of the U-matrix visualization approach with the new introduced fish glyph, the SOM can be displayed as a underwater scenario. Data sets can be visualized in the SOM as swarms of fishes or prototypes. The SOM reef provides (i) a powerful me...

I dette foredraget vil vi presentere et prosjekt som nettopp har kommet i gang, og som har fått NOK 1 000 000 fra NorFA til et nordisk nettverk. Prosjektet har to siktemål som henger nøye sammen. Det ene er å bygge nettverk mellom fire forskningsog utviklingsmiljøer ved UiO (Tekstlaboratoriet), UiB (HIT-senteret), GU (Språkdata) og CST. Det andre er praktisk: å utvikle en automatisk navnegjenkj...

CCK and gastrin stimulate somatostatin (SOM) secretion and thus modulate their direct effects on the parietal cell. Although SOM is stored in D cells of the fundus and antrum, the nature of the cell type differs, and it is not known whether both regions respond to the stimulatory effects of CCK and gastrin. The objectives of the present study were to determine the separate effects of CCK and ga...

The present study provides a detailed description of somatostatin (SOM) distribution and the colocalization pattern of SOM, neuropeptide Y (NPY) and nitric oxide synthase (NOS) in the dorsal striatum (caudate-putamen complex) of the guinea pig. Within the dorsal striatum, SOM is found in a population of medium-sized aspiny interneurons. We found that 97% of all SOM-IR neurons expressed NPY simu...

Two numerical methods are proposed to solve the electric impedance tomography (EIT) problem in a domain with arbitrary boundary shape. The first is the new fast Fourier transform subspace-based optimization method (NFFT-SOM). Instead of implementing optimization within the subspace spanned by smaller singular vectors in subspace-based optimization method (SOM), a space spanned by complete Fouri...

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-e...

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that micro...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A theoretical and practical challenge in the SOM has been the diffi­ culty to treat the method as a statistical model fitting procedure. In this chapter we give a short review of statistical approaches for the SOM. Then we present the probability density model for which the SOM training gives the maximum likeli­ h...

Recently, clustering algorithms combined conventional methods and artificial intelligence. FSCSOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster ...