Publikation: Engineering Graph Clustering : Models and Experimental Evaluation
Lade...
Dateien
Datum
2007
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
ACM Journal of Experimental Algorithmics. 2007, 12, 1.1. Available under: doi: 10.1145/1227161.1227162
Zusammenfassung
A promising approach to graph clustering is based on the intuitive notion of intracluster density versus intercluster sparsity. As for the weighted case, clusters should accumulate lots of weight, in contrast to their connection to the remaining graph, which should be light. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed, no conclusive argument on their appropriateness has been given. In order to deepen the understanding of particular concepts, including both quality assessment as well as designing new algorithms, we conducted an experimental evaluation of graph-clustering approaches. By combining proved techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
algorithm, experimental evaluation, quality measures, clustering algorithms, design, graph clustering
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
BRANDES, Ulrik, Marco GAERTLER, Dorothea WAGNER, 2007. Engineering Graph Clustering : Models and Experimental Evaluation. In: ACM Journal of Experimental Algorithmics. 2007, 12, 1.1. Available under: doi: 10.1145/1227161.1227162BibTex
@article{Brandes2007Engin-5928,
year={2007},
doi={10.1145/1227161.1227162},
title={Engineering Graph Clustering : Models and Experimental Evaluation},
volume={12},
journal={ACM Journal of Experimental Algorithmics},
author={Brandes, Ulrik and Gaertler, Marco and Wagner, Dorothea},
note={Article Number: 1.1}
}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/123456789/5928">
<dc:creator>Gaertler, Marco</dc:creator>
<dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:01:26Z</dc:date>
<dc:format>application/pdf</dc:format>
<dcterms:title>Engineering Graph Clustering : Models and Experimental Evaluation</dcterms:title>
<dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5928/1/clustering.pdf"/>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dcterms:bibliographicCitation>First publ. in: ACM Journal of Experimental Algorithmics 12 (2007), Article 1.1</dcterms:bibliographicCitation>
<bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5928"/>
<dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/>
<dc:creator>Brandes, Ulrik</dc:creator>
<dcterms:abstract xml:lang="eng">A promising approach to graph clustering is based on the intuitive notion of intracluster density versus intercluster sparsity. As for the weighted case, clusters should accumulate lots of weight, in contrast to their connection to the remaining graph, which should be light. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed, no conclusive argument on their appropriateness has been given. In order to deepen the understanding of particular concepts, including both quality assessment as well as designing new algorithms, we conducted an experimental evaluation of graph-clustering approaches. By combining proved techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.</dcterms:abstract>
<dc:language>eng</dc:language>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:contributor>Wagner, Dorothea</dc:contributor>
<dc:creator>Wagner, Dorothea</dc:creator>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:01:26Z</dcterms:available>
<dc:contributor>Gaertler, Marco</dc:contributor>
<dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5928/1/clustering.pdf"/>
<dcterms:issued>2007</dcterms:issued>
<dc:contributor>Brandes, Ulrik</dc:contributor>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
</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
