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.
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Gansner et al. (2010) studied this question.
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