Publikation:

Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2014

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Informatik und Informationswissenschaft: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. 2014

Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

Informatik 2014 - Big Data : Komplexität meistern, 22. Sept. 2014 - 26. Sept. 2014, Stuttgart
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SHAO, 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. 2014
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.}
}
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>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen