Andrews curves are examples of the space transformed visualization (STV) techniques for visualizing multivariate data, which represent k‐dimensional data points by a profile line (or curve) in two‐ or three‐dimensional space using orthogonal basis functions. Andrews curves are based on Fourier series where the coefficients are the observation's values. One advantage of the plot is based on the Parseval's identity (energy norm), which indicates that the information through transformation from the data space into the parameter space is preserved, and information that can be deduced in the hyperdimensional original space can be easily deduced in the two‐dimensional parameter space. This duality empowers the discovery of correlated records, clusters and outliers based on the curve's intersections, gaps and isolations, respectively. This article focuses on STV, in general, Andrews curves visualizations, in particular, and the effective use of these methods in the exploration of clusters, classes, and outliers. WIREs Comp Stat 2011 3 373–382 DOI: 10.1002/wics.160
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