Scientific data visualization using three-dimensional self-organizing feature maps

The goal of scientific data visualization is to transform numeric or symbolic data into simple coherent patterns for enhanced human interpretation. It involves a combination of exploratory data analysis and data visualization techniques that create a new level of information providing a deeper look at the underlying structures present in high dimensional data. This paper discusses how a spherical self-organizing feature map (SOFM) enables multivariate numeric data to take a geometric form by mapping high dimensional data to a 3D, space, thereby providing a mechanism to explore large numeric databases for coherent patterns. The patterns present in the numeric data are given a shape based on similarity. The performance of the proposed visualization algorithm is tested using coordinate data from known geometry and multi-spectral satellite data.

[1]  David S. Ebert,et al.  A Shape-Based Visual Interface for Text Retrieval , 1999, IEEE Computer Graphics and Applications.

[2]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[3]  Katsushi Ikeuchi,et al.  Spherical Representations: for EGI to SAI , 1994, Object Representation in Computer Vision.

[4]  H. Ritter.  Self-Organizing Maps on non-euclidean Spaces , 1999 .

[5]  Markus H. Gross.  Visual computing - the integration of computer graphics, visual perception and imaging , 1994, Computer graphics: systems and applications.

[6]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[7]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH 1999.

[8]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH Courses.

[9]  Stanley C. Ahalt,et al.  Neural Networks for Vector Quantization of Speech and Images , 1990, IEEE J. Sel. Areas Commun..

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..