DocumentCode :
1317314
Title :
Visualizing Graphs and Clusters as Maps
Author :
Gansner, E.R. ; Yifan Hu ; Kobourov, S.G.
Author_Institution :
AT&TLabs Res., Florham Park, NJ, USA
Volume :
30
Issue :
6
fYear :
2010
Firstpage :
54
Lastpage :
66
Abstract :
Information visualization is essential in making sense of large datasets. Often, high-dimensional data are visualized as a collection of points in 2D space through dimensionality reduction techniques. However, these traditional methods often don´t capture the underlying structural information, clustering, and neighborhoods well. GMap is a practical algorithmic framework for visualizing relational data with geographic-like maps. This approach is effective in various domains.
Keywords :
cartography; data reduction; data visualisation; GMap; dimensionality reduction techniques; geographic-like maps; high-dimensional data; information visualization; large datasets; structural information; visualizing graphs; Books; Clustering algorithms; Data visualization; Ethics; History; Sparks; Writing; clustering; computer graphics; graph coloring; graph drawing; graphics and multimedia; information visualization; maps; set visualization;
fLanguage :
English
Journal_Title :
Computer Graphics and Applications, IEEE
Publisher :
ieee
ISSN :
0272-1716
Type :
jour
DOI :
10.1109/MCG.2010.101
Filename :
5567116
Link To Document :
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