Self-organizing maps
The VRML 2.0 sourcebook (2nd ed.)
The VRML 2.0 sourcebook (2nd ed.)
Information visualization: perception for design
Information visualization: perception for design
Visualization of high-dimensional model characteristics
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Information visualization in data mining and knowledge discovery
Information visualization in data mining and knowledge discovery
A Shape-Based Visual Interface for Text Retrieval
IEEE Computer Graphics and Applications
A Spherical Representation for Recognition of Free-Form Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualizing web search results using glyphs: Design and evaluation of a flower metaphor
ACM Transactions on Management Information Systems (TMIS)
Visualizing alternative scenarios of evolution in heritage architecture
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Design of multi-dimensional transfer functions using dimensional reduction
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
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Scientific data visualization provides scientists and engingeers with a deeper insight and greater understanding about physical phenomena through the use of graphical tools. Individuals are able to identify patterns embedded in data sets using visual cues such as color and shape, rather than directly searching through a vast volume of numbers. The visualization algorithm described in this paper utilizes a spherical self-organizing feature map (SOFM) to automatically cluster and develop a well-defined topology of arbitrary data vectors, based on a pre-defined measure of similarity, and generate a three-dimensional color-coded surface model that reflects cluster variations. Implementation of this self-organizing surface geometry for data visualization applications is illustrated by examining the graphical forms created for a small synthetic test data set and a large environmental data-base. The proposed methodology provides the researcher with a new tool to encode information into shape and easily transfer the geometric model to an immersive virtual reality (IVR) environment for interactive information analysis.