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).
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© 2012 Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden
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Runkler, T. (2012). Data Visualization. In: Data Analytics. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-8348-2589-6_4
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DOI: https://doi.org/10.1007/978-3-8348-2589-6_4
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