Computer technologies have been rapidly improving throughout the last couple of decades, and they are now at the stage of allowing scientists to carry out data analyses that deal with very complex and multivariate datasets. Moreover, there are growing numbers of researchers who wish to carry out such tasks in real-time. Traditional data analyses and visualization techniques are useful but not s...

This paper presents a neural-network based calibration method for measuring the 3D eye-gaze position in a stereoscopic display. The method is based on depth computation through local non-linear regression and interpolation using a parameterized self-organizing map (PSOM). A novel, efficient 3D calibration method for this application is introduced. We report an experiment in which we compare the...

Collaborative ventures—both equity-based partnerships as well as project-based alliances—have dominated the international business scene over the past two decades. By means of this study we investigate the patterns of related and unrelated collaborative venture formation. Using a large database of over 90,000 collaborative ventures formed during the 1985–2001 period, this study clusters collabo...

In exploratory analysis of high-dimensional data the self-organizing map can be used to illustrate relations between the data items. We have developed two measures for comparing how diierent maps represent these relations. The other combines an index of discontinuities in the mapping from the input data set to the map grid with an index of the accuracy with which the map represents the data set...

We show that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization. By reviewing the appropriate literature and theory and own empirical results, we demonstrate that SOMs can be used for clustering or visualization separately, for simultaneous clustering and visualization, and even for clustering via visualization. For...

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on ...

One of the biggest problems facing practical applications of Self-Organising Maps (SOM) is their dependence on the learning rate, the size of the neighbourhood function and the decrease of these parameters as training progresses, all of which have to be selected without firm theoretical guidance. This paper introduces a simple modification to the SOM that completely eliminates the learning rate...