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Andrews curves
Author(s) -
Moustafa Rida E.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.160
Subject(s) - outlier , plot (graphics) , scatter plot , visualization , basis (linear algebra) , transformation (genetics) , space (punctuation) , mathematics , graphics , data visualization , computer science , algorithm , data mining , geometry , statistics , computer graphics (images) , biochemistry , chemistry , gene , operating system
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 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization