Name
Abstract | ||
|---|---|---|
Information visualization is essential in making sense out of large data
sets. Often, high-dimensional data are visualized as a collection of points in
2-dimensional space through dimensionality reduction techniques. However, these
traditional methods often do not capture well the underlying structural
information, clustering, and neighborhoods. In this paper, we describe GMap: a
practical tool for visualizing relational data with geographic-like maps. We
illustrate the effectiveness of this approach with examples from several
domains All the maps referenced in this paper can be found in
http://www.research.att.com/~yifanhu/GMap |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-11805-0_38 | Clinical Orthopaedics and Related Research |
Keywords | DocType | Volume |
high dimensional data,information visualization,computational geometry,relational data,2 dimensional | Conference | abs/0907.2585 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-11804-6 | 9 |
PageRank | References | Authors |
0.49 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emden R. Gansner | 1 | 1117 | 115.32 |
| Yifan Hu | 2 | 1480 | 88.96 |
| Stephen G. Kobourov | 3 | 1440 | 122.20 |