Beyond the Third Dimension: Visualizing High-Dimensional Data with Projections

Multidimensional projections are an increasingly popular technique for visualizing large datasets containing observations having tens or even hundreds of dimensions. Compared to other techniques such as parallel coordinates, tables, and scatterplot matrices, they support tasks such as finding groups of related observations and outliers in simpler, more effective, ways. The authors discuss here the advantages of multidimensional projections, how to compute them, and recent advances that enhance them by visual explanatory techniques, so as to make them efficient and effective instruments that should be part of the toolkit of any scientist interested in high-dimensional data exploration.