Publikation:

Visual cluster analysis of trajectory data with interactive Kohonen maps

Lade...
Vorschaubild

Dateien

Schreck.pdf
Schreck.pdfGröße: 8.19 MBDownloads: 2039

Datum

2009

Autor:innen

Bernard, Jürgen
von Landesberger, Tatiana
Kohlhammer, Jörn

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Information Visualization. 2009, 8(1), pp. 14-29. ISSN 1473-8716. Available under: doi: 10.1057/ivs.2008.29

Zusammenfassung

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Visual analytics, visual cluster analysis, self-organizing maps, trajectory data, time series data

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SCHRECK, Tobias, Jürgen BERNARD, Tatiana VON LANDESBERGER, Jörn KOHLHAMMER, 2009. Visual cluster analysis of trajectory data with interactive Kohonen maps. In: Information Visualization. 2009, 8(1), pp. 14-29. ISSN 1473-8716. Available under: doi: 10.1057/ivs.2008.29
BibTex
@article{Schreck2009Visua-17389,
  year={2009},
  doi={10.1057/ivs.2008.29},
  title={Visual cluster analysis of trajectory data with interactive Kohonen maps},
  number={1},
  volume={8},
  issn={1473-8716},
  journal={Information Visualization},
  pages={14--29},
  author={Schreck, Tobias and Bernard, Jürgen and von Landesberger, Tatiana and Kohlhammer, Jörn}
}
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/123456789/17389">
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/17389/1/Schreck.pdf"/>
    <dc:contributor>Kohlhammer, Jörn</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Kohlhammer, Jörn</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Bernard, Jürgen</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:issued>2009</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-01-31T12:37:24Z</dcterms:available>
    <dc:contributor>von Landesberger, Tatiana</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-01-31T12:37:24Z</dc:date>
    <dc:language>eng</dc:language>
    <dcterms:title>Visual cluster analysis of trajectory data with interactive Kohonen maps</dcterms:title>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>von Landesberger, Tatiana</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/17389/1/Schreck.pdf"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/17389"/>
    <dc:contributor>Bernard, Jürgen</dc:contributor>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: Information Visualization ; 8 (2009), 1. - pp. 14-29</dcterms:bibliographicCitation>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:abstract xml:lang="eng">Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.</dcterms:abstract>
    <dc:creator>Schreck, Tobias</dc:creator>
  </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
Nein
Begutachtet
Diese Publikation teilen