Publikation: Selecting good views of high-dimensional data using class consistency
Lade...
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2009
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Computer Graphics Forum. 2009, 28(3), pp. 831-838. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/j.1467-8659.2009.01467.x
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
EuroVis, 2009
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
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. 2009, 28(3), pp. 831-838. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/j.1467-8659.2009.01467.xBibTex
@article{Sips2009-06Selec-3004,
year={2009},
doi={10.1111/j.1467-8659.2009.01467.x},
title={Selecting good views of high-dimensional data using class consistency},
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:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/3004">
<dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:creator>Neubert, Boris</dc:creator>
<dc:creator>Sips, Mike</dc:creator>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/3004"/>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:44Z</dcterms:available>
<dc:contributor>Sips, Mike</dc:contributor>
<dcterms:issued>2009-06</dcterms:issued>
<dc:rights>terms-of-use</dc:rights>
<dcterms:title>Selecting good views of high-dimensional data using class consistency</dcterms:title>
<dc:creator>Lewis, John P.</dc:creator>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dc:contributor>Hanrahan, Pat</dc:contributor>
<dc:contributor>Neubert, Boris</dc:contributor>
<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:contributor>Lewis, John P.</dc:contributor>
<dc:creator>Hanrahan, Pat</dc:creator>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:44Z</dc:date>
<dcterms:bibliographicCitation>Publ. in: Computer graphics forum, 28 (2009), 3, pp. 831-838 (EuroVis 09)</dcterms:bibliographicCitation>
<dc:language>eng</dc:language>
</rdf:Description>
</rdf:RDF>Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
