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Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data

Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data

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TATU, Andrada, Georgia ALBUQUERQUE, Martin EISEMANN, Peter BAK, Holger THEISEL, Marcus MAGNOR, Daniel KEIM, 2011. Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data. In: IEEE Transactions on Visualization and Computer Graphics. 17(5), pp. 584-597. ISSN 1077-2626. eISSN 1941-0506

@article{Tatu2011Autom-13655, title={Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data}, year={2011}, doi={10.1109/TVCG.2010.242}, number={5}, volume={17}, issn={1077-2626}, journal={IEEE Transactions on Visualization and Computer Graphics}, pages={584--597}, author={Tatu, Andrada and Albuquerque, Georgia and Eisemann, Martin and Bak, Peter and Theisel, Holger and Magnor, Marcus and Keim, Daniel} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/13655"> <dcterms:bibliographicCitation>First publ. in: IEEE Transactions on Visualization and Computer Graphics ; 17 (2011), 5. - S. 584-597</dcterms:bibliographicCitation> <dc:creator>Theisel, Holger</dc:creator> <dc:creator>Albuquerque, Georgia</dc:creator> <dcterms:issued>2011</dcterms:issued> <dc:contributor>Keim, Daniel</dc:contributor> <dc:contributor>Eisemann, Martin</dc:contributor> <dc:contributor>Bak, Peter</dc:contributor> <dc:creator>Eisemann, Martin</dc:creator> <dcterms:title>Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data</dcterms:title> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/13655"/> <dc:contributor>Theisel, Holger</dc:contributor> <dc:contributor>Tatu, Andrada</dc:contributor> <dc:creator>Keim, Daniel</dc:creator> <dcterms:abstract xml:lang="eng">Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.</dcterms:abstract> <dc:creator>Bak, Peter</dc:creator> <dc:rights>deposit-license</dc:rights> <dc:language>eng</dc:language> <dc:contributor>Magnor, Marcus</dc:contributor> <dc:contributor>Albuquerque, Georgia</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-07-14T12:17:39Z</dc:date> <dc:creator>Magnor, Marcus</dc:creator> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-07-14T12:17:39Z</dcterms:available> <dc:creator>Tatu, Andrada</dc:creator> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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