TY - GEN
T1 - GMap
T2 - IEEE Pacific Visualization Symposium 2010, PacificVis 2010
AU - Gansner, Emden R.
AU - Hu, Yifan
AU - Kobourov, Stephen
PY - 2010
Y1 - 2010
N2 - 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 algorithm for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains.
AB - 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 algorithm for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains.
KW - G.2 [discrete mathematics]: Graph theory - [H.3]: Information storage and retrieval - Clustering
UR - http://www.scopus.com/inward/record.url?scp=77951694401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951694401&partnerID=8YFLogxK
U2 - 10.1109/PACIFICVIS.2010.5429590
DO - 10.1109/PACIFICVIS.2010.5429590
M3 - Conference contribution
AN - SCOPUS:77951694401
SN - 9781424466849
T3 - IEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings
SP - 201
EP - 208
BT - IEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings
Y2 - 2 March 2010 through 5 March 2010
ER -