2010, S. Kontaxaki, E. Tomai, M. Kokla, and M. Kavouras, "Visualizing multidimensional data through granularity-dependent spatialization" Print

 

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Kontaxaki, S., Tomai, E., Kokla, M., and Kavouras, M., "Visualizing multidimensional data through granularity-dependent spatialization", In: Proceedings of the SPIE-IS&T Electronic Imaging, Conference on Visualization and Data Analysis 2010, doi: 10.1117/12.838430, SPIE Vol. 7530, 75300M, 2010.

 

Abstract:

 

Spatialization is a special kind of visualization that projects multidimensional data into low-dimensional representational spaces by making use of spatial metaphors. Spatialization methods face a dual challenge: on the one hand, to apply dimension reduction techniques in order to overcome the limitations of the representational space, and on the other hand, to provide a metaphoric framework for the visualization of information at different levels of granularity. This paper investigates how granularity is modeled and visualized by the existing spatialization methods, and introduces a new approach based on kernel density estimation and landscape metaphor. According to this approach, clusters of multidimensional data are revealed by landscape “relief”, and are hierarchically organized into different levels of granularity through landscape “smoothness.” In addition, it is demonstrated how the exploration of information at different levels of granularity is supported by appropriate operations in the framework of an interactive spatialization.

environment prototype.

 

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