Brief outlines of exploratory analysis methods (analysis designed to develop hypotheses) from three research projects illustrate the size, scope, variety and increased resolution that are becoming increasingly available at the unit of analysis for research in the learning sciences. The tools and methods applied in these studies are briefly outlined, which enable researchers to deal with complex...

The self-organizing map (SOM) is widely used as a data visualization method especially in various engineering applications. It performs a non-linear mapping from a high-dimensional data space to a lower dimensional visualization space. The SOM can be used for example in correlation detection and cluster visualization in explorative manner. In this paper two tools for reeng the SOM-based visuali...

Geometric independent component analysis (ICA) uses a weight update rule that is very similar to the self-organizing map (SOM) learning rule in the case of a trivial neighborhood function. In this paper we use this fact and present a new geometric ICA algorithm that uses a SOM for learning. The separation qualiry is better in comparison to other geometric algorithms, but the computational cost ...

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...

Clustering algorithm for the moving or trajectory data provides new and helpful information. It has wide application on various location aware services. In this study the Self Organizing Map is used to form the cluster on trajectory data. The self-organizing map (SOM) is an important tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular gri...

Self-Organizing Map and Growing Self-Organizing Map are widely used techniques for exploratory data analysis. The key desirable features of these techniques are applicability to real world data sets and the ability to visualize high dimensional data in low dimensional output space. One of the core problems of using SOM/GSOM based techniques on large datasets is the high processing time requirem...

The average mutual information (AMI) known from information theory has been reported as a strong genome signature in some literature and we have reported the use of oligonucleotide frequencies as a genome signature. In this work we improve the use of AMI as a training feature for Growing Self Organising Maps (GSOM). Although the range of k is considered as an important parameter in AMI, no stan...

We present a system for content-based retrieval of perceptually similar sound events in audio documents (‘sound spotting’, using a query by example. The system consists of three discrete stages: a front-end for feature extraction, a self-organizing map, and a pattern matching unit. Our paper introduces the approach, describes the separate modules and discusses some preliminary results and futur...