Eigensolver Methods for Progressive Multidimensional Scaling of Large Data
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| Publikationstyp: | Beitrag zu einer Konferenz |
| URI (zitierfähiger Link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-73423 |
| Autor/innen: | Brandes, Ulrik; Pich, Christian |
| Erscheinungsjahr: | 2007 |
| Erschienen in: | Graph Drawing / Kaufmann, Michael; Wagner, Dorothea (Hrsg.). - Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. - (Lecture Notes in Computer Science). - S. 42-53. - ISBN 978-3-540-70903-9 |
| DOI (zitierfähiger Link): | https://dx.doi.org/10.1007/978-3-540-70904-6_6 |
| Zusammenfassung: |
We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.
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| Fachgebiet (DDC): | 004 Informatik |
| Link zur Lizenz: | Nutzungsbedingungen |
| Universitätsbibliographie: | Ja |
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BRANDES, Ulrik, Christian PICH, 2007. Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In: KAUFMANN, Michael, ed., Dorothea WAGNER, ed.. Graph Drawing. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 42-53. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6
@inproceedings{Brandes2007Eigen-5741, title={Eigensolver Methods for Progressive Multidimensional Scaling of Large Data}, year={2007}, doi={10.1007/978-3-540-70904-6_6}, isbn={978-3-540-70903-9}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Graph Drawing}, pages={42--53}, editor={Kaufmann, Michael and Wagner, Dorothea}, author={Brandes, Ulrik and Pich, Christian} }
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Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)
| bp_empmdsld_06.pdf | 258 |
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