Clustering by principal curve with tree structure

Cleju, Ioan; Fränti, Pasi; Wu, Xiaolin

Clustering by principal curve with tree structure

Dateien

Cleju_230295.pdfGröße: 193.08 KBDownloads: 201

Datum

2005

Autor:innen

Cleju, Ioan

Fränti, Pasi

Wu, Xiaolin

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005.. IEEE, 2005, pp. 617-620. ISBN 0-7803-9029-6. Available under: doi: 10.1109/ISSCS.2005.1511316

Zusammenfassung

Data clustering is intensively used in signal processing in tasks such as multimedia compression, segmentation and pattern matching. In this work we extend the use of principal curves in clustering to complex multidimensional datasets. The use of principal curve in clustering is limited for high complexity data. Automatic parameterization of the principal curve to assure good results for different datasets is a difficult task. We propose to use the tree structure to capture the general settlement of the data. Using this topology, regions of the dataset can be extracted, individually clustered using the principal curve and then optimally recombined. The experiments show the improvement of the new method over the principal curve based clustering and the good performance compared to other clustering methods.

Fachgebiet (DDC)

004 Informatik

Konferenz

International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005., Iasi, Romania

Zitieren

ISO 690

CLEJU, Ioan, Pasi FRÄNTI, Xiaolin WU, 2005. Clustering by principal curve with tree structure. International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005.. Iasi, Romania. In: International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005.. IEEE, 2005, pp. 617-620. ISBN 0-7803-9029-6. Available under: doi: 10.1109/ISSCS.2005.1511316

BibTex

@inproceedings{Cleju2005Clust-23029,
  year={2005},
  doi={10.1109/ISSCS.2005.1511316},
  title={Clustering by principal curve with tree structure},
  isbn={0-7803-9029-6},
  publisher={IEEE},
  booktitle={International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005.},
  pages={617--620},
  author={Cleju, Ioan and Fränti, Pasi and Wu, Xiaolin}
}

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/123456789/23029">
    <dcterms:bibliographicCitation>Proceedings - ISSCS 2005, International Symposium on Signals, Circuits and Systems : July 14 - 15, 2005, Iasi, Romania ; vol. 2 / organized by Faculty of Electronics and Telecommunications ... - Piscataway, NJ : IEEE Operations Center, 2005. - S. 617-620. - ISBN 0-7803-9029-6</dcterms:bibliographicCitation>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-06-25T09:06:08Z</dcterms:available>
    <dc:creator>Cleju, Ioan</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:language>eng</dc:language>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:issued>2005</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Clustering by principal curve with tree structure</dcterms:title>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/23029/1/Cleju_230295.pdf"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Wu, Xiaolin</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-06-25T09:06:08Z</dc:date>
    <dc:contributor>Cleju, Ioan</dc:contributor>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/23029"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/23029/1/Cleju_230295.pdf"/>
    <dc:contributor>Fränti, Pasi</dc:contributor>
    <dc:creator>Fränti, Pasi</dc:creator>
    <dcterms:abstract xml:lang="eng">Data clustering is intensively used in signal processing in tasks such as multimedia compression, segmentation and pattern matching. In this work we extend the use of principal curves in clustering to complex multidimensional datasets. The use of principal curve in clustering is limited for high complexity data. Automatic parameterization of the principal curve to assure good results for different datasets is a difficult task. We propose to use the tree structure to capture the general settlement of the data. Using this topology, regions of the dataset can be extracted, individually clustered using the principal curve and then optimally recombined. The experiments show the improvement of the new method over the principal curve based clustering and the good performance compared to other clustering methods.</dcterms:abstract>
    <dc:contributor>Wu, Xiaolin</dc:contributor>
  </rdf:Description>
</rdf:RDF>