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Data Visualization

  • Thomas A. Runkler
Chapter

Abstract

Data can often be very effectively analyzed using visualization techniques. Standard visualization methods for object data are plots and scatter plots. To visualize high-dimensional data, projection methods are necessary. We present linear projection (principal component analysis, Karhunen-Lo`eve transform, singular value decomposition, eigenvector projection, Hotelling transform, proper orthogonal decomposition) and nonlinear projection methods (multidimensional scaling, Sammon mapping, auto-associator). Data distributions can be estimated and visualized using histogram techniques. Periodic time series can be analyzed and visualized using spectral analysis (cosine and sine transforms, amplitude and phase spectra).

Keywords

Principal Component Analysis Proper Orthogonal Decomposition Data Visualization Projection Error Cosine Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden 2012

Authors and Affiliations

  1. 1.MünchenGermany

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