Multiscale visual quality assessment for cluster analysis with self-organizing maps

Lade...
Vorschaubild
Dateien
Schreck_multiscale.pdf
Schreck_multiscale.pdfGröße: 10.56 MBDownloads: 745
Datum
2011
Autor:innen
Bernard, Jürgen
Landesberger, Tatiana von
Bremm, Sebastian
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
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
WONG, Pak Chung, ed. and others. Visualization and Data Analysis 2011. SPIE, 2011, pp. 78680N-78680N-12. SPIE Proceedings. 7868. Available under: doi: 10.1117/12.872545
Zusammenfassung

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many di erent clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with re ned parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual
cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We de ne, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Visual Cluster Analysis, Self-Organizing Maps, Cluster Comparison, Quality Visualization and Assessment, Visual Analysis
Konferenz
IS&T/SPIE Electronic Imaging, San Francisco, California
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690BERNARD, Jürgen, Tatiana von LANDESBERGER, Sebastian BREMM, Tobias SCHRECK, 2011. Multiscale visual quality assessment for cluster analysis with self-organizing maps. IS&T/SPIE Electronic Imaging. San Francisco, California. In: WONG, Pak Chung, ed. and others. Visualization and Data Analysis 2011. SPIE, 2011, pp. 78680N-78680N-12. SPIE Proceedings. 7868. Available under: doi: 10.1117/12.872545
BibTex
@inproceedings{Bernard2011-01-24Multi-16618,
  year={2011},
  doi={10.1117/12.872545},
  title={Multiscale visual quality assessment for cluster analysis with self-organizing maps},
  number={7868},
  publisher={SPIE},
  series={SPIE Proceedings},
  booktitle={Visualization and Data Analysis 2011},
  pages={78680N--78680N-12},
  editor={Wong, Pak Chung},
  author={Bernard, Jürgen and Landesberger, Tatiana von and Bremm, Sebastian and Schreck, Tobias}
}
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/123456789/16618">
    <dc:creator>Bremm, Sebastian</dc:creator>
    <dcterms:title>Multiscale visual quality assessment for cluster analysis with self-organizing maps</dcterms:title>
    <dc:rights>terms-of-use</dc:rights>
    <dc:language>eng</dc:language>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:contributor>Bernard, Jürgen</dc:contributor>
    <dcterms:issued>2011-01-24</dcterms:issued>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:abstract xml:lang="eng">Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many di erent clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with re ned parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual&lt;br /&gt;cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We de ne, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.</dcterms:abstract>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Landesberger, Tatiana von</dc:contributor>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/16618"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-11-08T11:13:05Z</dc:date>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-01-31T23:25:15Z</dcterms:available>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/16618/1/Schreck_multiscale.pdf"/>
    <dc:contributor>Bremm, Sebastian</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: Visualization and data analysis 2011 : 24 - 25 January 2011, California, United States ; [part of] IS&amp;T/SPIE electronic imaging, science and technology / sponsored and publ. by IS&amp;T - the Society for Imaging Science and Technology; SPIE. Pak Chung Wong ... (Eds.). - Bellingham, Wash. : SPIE [u.a.], 2011. - pp. 7868 0N. -  (Proceedings of SPIE ; 7868). - ISBN 978-0-8194-8405-5</dcterms:bibliographicCitation>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/16618/1/Schreck_multiscale.pdf"/>
    <dc:creator>Bernard, Jürgen</dc:creator>
    <dc:creator>Landesberger, Tatiana von</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
Ja
Begutachtet
Diese Publikation teilen