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
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;
Journal_Title :
Computer Graphics and Applications, IEEE
DOI :
10.1109/MCG.2010.101