作者: Emden R. Gansner , Yifan Hu , Stephen Kobourov
DOI: 10.1109/PACIFICVIS.2010.5429590
关键词: Dimensionality reduction 、 Graph theory 、 Cluster analysis 、 Theoretical computer science 、 Data mining 、 Computer science 、 Data visualization 、 Information visualization 、 Relational database 、 Algorithm design 、 Visualization
摘要: Information visualization is essential in making sense out of large data sets. Often, high-dimensional are visualized as a collection points 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, practical algorithm for visualizing relational with geographic-like maps. We illustrate effectiveness approach examples from several domains.