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Proceedings Paper

Visualizing multidimensional data through granularity-dependent spatialization
Author(s): Sofia Kontaxaki; Eleni Tomai; Margarita Kokla; Marinos Kavouras
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Paper 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 our 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, herein, how the exploration of information at different levels of granularity is supported by appropriate operations in the framework of an interactive spatialization environment prototype.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7530, Visualization and Data Analysis 2010, 75300M (18 January 2010); doi: 10.1117/12.838430
Show Author Affiliations
Sofia Kontaxaki, National Technical Univ. of Athens (Greece)
Eleni Tomai, National Technical Univ. of Athens (Greece)
Margarita Kokla, National Technical Univ. of Athens (Greece)
Marinos Kavouras, National Technical Univ. of Athens (Greece)

Published in SPIE Proceedings Vol. 7530:
Visualization and Data Analysis 2010
Jinah Park; Ming C. Hao; Pak Chung Wong; Chaomei Chen, Editor(s)

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