Selecting good views of high-dimensional data using class consistency

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SIPS, Mike, Boris NEUBERT, John P. LEWIS, Pat HANRAHAN, 2009. Selecting good views of high-dimensional data using class consistency. EuroVis, 2009. In: Computer Graphics Forum. EuroVis, 2009. 28(3), pp. 831-838. ISSN 0167-7055. eISSN 1467-8659

@article{Sips2009-06Selec-3004, title={Selecting good views of high-dimensional data using class consistency}, year={2009}, doi={10.1111/j.1467-8659.2009.01467.x}, number={3}, volume={28}, issn={0167-7055}, journal={Computer Graphics Forum}, pages={831--838}, author={Sips, Mike and Neubert, Boris and Lewis, John P. and Hanrahan, Pat} }

<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/123456789/3004"> <dc:contributor>Lewis, John P.</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103416863-3868037-7"/> <dcterms:bibliographicCitation>Publ. in: Computer graphics forum, 28 (2009), 3, pp. 831-838 (EuroVis 09)</dcterms:bibliographicCitation> <dc:creator>Neubert, Boris</dc:creator> <dc:creator>Hanrahan, Pat</dc:creator> <dcterms:title>Selecting good views of high-dimensional data using class consistency</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:44Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:44Z</dcterms:available> <dcterms:issued>2009-06</dcterms:issued> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/3004"/> <dc:contributor>Neubert, Boris</dc:contributor> <dc:contributor>Hanrahan, Pat</dc:contributor> <dc:creator>Sips, Mike</dc:creator> <dcterms:abstract xml:lang="eng">Many visualization techniques involve mapping high-dimensional data spaces to lower-dimensional views. Unfortunately, mapping a high-dimensional data space into a scatterplot involves a loss of information; or, even worse, it can give a misleading picture of valuable structure in higher dimensions. In this paper, we propose class consistency as a measure of the quality of the mapping. Class consistency enforces the constraint that classes of n D data are shown clearly in 2 D scatterplots. We propose two quantitative measures of class consistency, one based on the distance to the class s center of gravity, and another based on the entropies of the spatial distributions of classes. We performed an experiment where users choose good views, and show that class consistency has good precision and recall. We also evaluate both consistency measures over a range of data sets and show that these measures are efficient and robust.</dcterms:abstract> <dc:creator>Lewis, John P.</dc:creator> <dc:language>eng</dc:language> <dc:rights>deposit-license</dc:rights> <dc:contributor>Sips, Mike</dc:contributor> </rdf:Description> </rdf:RDF>

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