Clustering based on principal curve

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CLEJU, Ioan, Pasi FRÄNTI, Xiaolin WU, 2005. Clustering based on principal curve. In: KALVIAINEN, Heikki, ed., Jussi PARKKINEN, ed., Arto KAARNA, ed.. Image Analysis. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 872-881. ISBN 978-3-540-26320-3

@inproceedings{Cleju2005Clust-23030, title={Clustering based on principal curve}, year={2005}, doi={10.1007/11499145_88}, number={3540}, isbn={978-3-540-26320-3}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Image Analysis}, pages={872--881}, editor={Kalviainen, Heikki and Parkkinen, Jussi and Kaarna, Arto}, author={Cleju, Ioan and Fränti, Pasi and Wu, Xiaolin} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/23030"> <dc:creator>Fränti, Pasi</dc:creator> <dc:language>eng</dc:language> <dcterms:bibliographicCitation>Image Analysis : 14th Scandinavian Conference, SCIA, 2005, Joensuu, Finland, June 19 - 22, 2005; proceedings / Heikki Kalviainen ... (eds.). - Berlin [u.a.] : Springer, 2005. - S. 872-881. - (Lecture notes in computer science ; 3540). - ISBN 978-3-540-26320-3</dcterms:bibliographicCitation> <dcterms:title>Clustering based on principal curve</dcterms:title> <dc:creator>Cleju, Ioan</dc:creator> <dc:contributor>Fränti, Pasi</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:issued>2005</dcterms:issued> <dcterms:abstract xml:lang="eng">Clustering algorithms are intensively used in the image analysis field in compression, segmentation, recognition and other tasks. In this work we present a new approach in clustering vector datasets by finding a good order in the set, and then applying an optimal segmentation algorithm. The algorithm heuristically prolongs the optimal scalar quantization technique to vector space. The data set is sequenced using one-dimensional projection spaces. We Show that the principal axis is too rigid to preserve the adjacency of the points. We present a way to refine the order using the minimum weight Hamiltonian path in the data graph. Next we propose to use the principal curve to better model the non-linearity of the data and find a good sequence in the data. The experimental results show that the principal curve based clustering method can be successfully used in cluster analysis.</dcterms:abstract> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/23030"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-06-10T09:08:32Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-06-10T09:08:32Z</dc:date> <dc:creator>Wu, Xiaolin</dc:creator> <dc:contributor>Wu, Xiaolin</dc:contributor> <dc:rights>deposit-license</dc:rights> <dc:contributor>Cleju, Ioan</dc:contributor> </rdf:Description> </rdf:RDF>

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