Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

Kurzanzeige
dc.contributor.authorShao, Lin
dc.contributor.authorBehrisch, Michael
dc.contributor.authorSchreck, Tobias
dc.contributor.authorSipiran, Ivan
dc.contributor.authorKwon, Bum Chul
dc.contributor.authorKeim, Daniel A.
dc.date.accessioned2015-03-11T16:03:56Z
dc.date.available2015-03-11T16:03:56Z
dc.date.issued2014eng
dc.description.abstractScatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.eng
dc.description.versionpublished
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/30219
dc.language.isoengeng
dc.subject.ddc004eng
dc.titleIdentifying Locally Interesting Motifs for Exploration of Scatter Plot Matriceseng
dc.typeINPROCEEDINGSdeu
dspace.entity.typePublication
kops.citation.bibtex
@inproceedings{Shao2014Ident-30219,
  year={2014},
  title={Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices},
  booktitle={GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany},
  author={Shao, Lin and Behrisch, Michael and Schreck, Tobias and Sipiran, Ivan and Kwon, Bum Chul and Keim, Daniel A.}
}
kops.citation.iso690SHAO, Lin, Michael BEHRISCH, Tobias SCHRECK, Ivan SIPIRAN, Bum Chul KWON, Daniel A. KEIM, 2014. Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices. Informatik 2014 - Big Data : Komplexität meistern. Stuttgart, 22. Sept. 2014 - 26. Sept. 2014. In: GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. 2014deu
kops.citation.iso690SHAO, Lin, Michael BEHRISCH, Tobias SCHRECK, Ivan SIPIRAN, Bum Chul KWON, Daniel A. KEIM, 2014. Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices. Informatik 2014 - Big Data : Komplexität meistern. Stuttgart, Sep 22, 2014 - Sep 26, 2014. In: GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. 2014eng
kops.citation.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/123456789/30219">
    <dc:creator>Shao, Lin</dc:creator>
    <dc:creator>Kwon, Bum Chul</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Sipiran, Ivan</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:title>Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Sipiran, Ivan</dc:creator>
    <dc:contributor>Behrisch, Michael</dc:contributor>
    <dc:contributor>Kwon, Bum Chul</dc:contributor>
    <dcterms:abstract xml:lang="eng">Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.</dcterms:abstract>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T16:03:56Z</dcterms:available>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30219"/>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:creator>Behrisch, Michael</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T16:03:56Z</dc:date>
    <dc:contributor>Shao, Lin</dc:contributor>
    <dcterms:issued>2014</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
  </rdf:Description>
</rdf:RDF>
kops.conferencefieldInformatik 2014 - Big Data : Komplexität meistern, 22. Sept. 2014 - 26. Sept. 2014, Stuttgartdeu
kops.date.conferenceEnd2014-09-26eng
kops.date.conferenceStart2014-09-22eng
kops.flag.knbibliographytrue
kops.location.conferenceStuttgarteng
kops.sourcefield<i>GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany</i>. 2014deu
kops.sourcefield.plainGI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. 2014deu
kops.sourcefield.plainGI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. 2014eng
kops.title.conferenceInformatik 2014 - Big Data : Komplexität meisterneng
relation.isAuthorOfPublication9aca8474-3869-4ef5-a0a9-a12ffb48312b
relation.isAuthorOfPublication9d4120c1-baeb-41e5-a9f3-72a9c39197a7
relation.isAuthorOfPublication79e07bb0-6b48-4337-8a5b-6c650aaeb29d
relation.isAuthorOfPublication2900bcf6-d9aa-440a-8f9d-b469f0fc1622
relation.isAuthorOfPublicationf6faa7de-ebbf-42f6-971e-42e24202c37d
relation.isAuthorOfPublicationda7dafb0-6003-4fd4-803c-11e1e72d621a
relation.isAuthorOfPublication.latestForDiscovery9aca8474-3869-4ef5-a0a9-a12ffb48312b
source.titleGI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germanyeng

Dateien

Sammlungen

Informatik und Informationswissenschaft: Publikationen