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Datenvisualisierung

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Data Mining

Part of the book series: Computational Intelligence ((CI))

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Zusammenfassung

Visualisierungstechniken lassen sich sehr effektiv zur Datenanalyse einsetzen. Standardmethoden sind Diagramme und Streudiagramme. Zur Visualisierung hochdimensionaler Daten müssen Projektionen durchgeführt werden. Es werden lineare (Hauptkomponentenanalyse, Karhunen-Loève-Transformation, Singulärwertzerlegung, Eigenvektorprojektion, Hotelling-Transformation, mehrdimensionale Skalierung) und nichtlineare Projektionsmethoden (Sammon-Abbildung, Auto-Assoziator) vorgestellt. Histogrammverfahren erlauben die Schätzung und Visualisierung von Datenverteilungen. Die Spektralanalyse (Kosinus- und Sinustransformation, Amplituden- und Phasenspektren) ermöglicht die Analyse und Visualisierung von periodischen Daten (z. B. Zeitreihen).

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Author information

Authors and Affiliations

  1. Siemens AG, München, Deutschland

    Thomas A. Runkler Prof. Dr.-Ing.

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  1. Thomas A. Runkler Prof. Dr.-Ing.
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Correspondence to Thomas A. Runkler Prof. Dr.-Ing. .

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© 2015 Springer Fachmedien Wiesbaden

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Runkler, T. (2015). Datenvisualisierung. In: Data Mining. Computational Intelligence. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-8348-2171-3_4

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