Visualizing multidimensional data through granularity-dependent Spatialization

Kontaxaki, S; Tomai, E; Kokla, M; Kavouras, M

dc.contributor.author	Kontaxaki, S	en
dc.contributor.author	Tomai, E	en
dc.contributor.author	Kokla, M	en
dc.contributor.author	Kavouras, M	en
dc.date.accessioned	2014-03-01T02:47:09Z
dc.date.available	2014-03-01T02:47:09Z
dc.date.issued	2010	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33017
dc.subject	Granularity-depended visualization	en
dc.subject	Information visualization	en
dc.subject	Spatialization	en
dc.subject.other	Dimension reduction techniques	en
dc.subject.other	Information visualization	en
dc.subject.other	Kernel Density Estimation	en
dc.subject.other	Multidimensional data	en
dc.subject.other	New approaches	en
dc.subject.other	Spatial metaphors	en
dc.subject.other	Spatialization	en
dc.subject.other	Visualization of information	en
dc.subject.other	Data handling	en
dc.subject.other	Information analysis	en
dc.subject.other	Information systems	en
dc.subject.other	Visualization	en
dc.subject.other	Data visualization	en
dc.title	Visualizing multidimensional data through granularity-dependent Spatialization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.838430	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.838430	en
heal.identifier.secondary	75300M	en
heal.publicationDate	2010	en
heal.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. © 2010 SPIE-IS&T.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.doi	10.1117/12.838430	en
dc.identifier.volume	7530	en