Subspace search and visualization to make sense of alternative clusterings in high-dimensional data

In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive m...

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Bibliographische Detailangaben

Veröffentlicht in:	2012 IEEE Conference on Visual Analytics Science and Technology S. 63 - 72
Hauptverfasser:	Tatu, A., Maas, F., Farber, I., Bertini, E., Schreck, T., Seidl, T., Keim, D.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2012
Schlagworte:	Algorithm design and analysis Clustering algorithms Data visualization Display algorithms Educational institutions H.2.8 [Database Applications]: Data mining H.3.3 [Information Search and Retrieval]: Selection process High definition video I.3.3 [Picture/Image Generation] Topology Visualization
ISBN:	1467347523, 9781467347525
Online-Zugang:	Volltext
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