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2020
Conference Paper
Titel

Shells within Minimum Enclosing Balls

Abstract
Addressing the general problem of data clustering, we propose to group the elements of a data set with respect to their location within their minimum enclosing ball. In particular, we propose to cluster data according to their distance to the center of a kernel minimum enclosing ball. Focusing on kernel minimum enclosing balls which are computed in abstract feature spaces reveals latent structures within a data set and allows for applying our ideas to non-numeric data. Results obtained on image-, text-, and graph-data illustrate the behavior and practical utility of our approach.
Author(s)
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Bortz, Michael
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Sifa, Rafet
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020. Proceedings
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Data Science and Advanced Analytics (DSAA) 2020
Thumbnail Image
DOI
10.1109/DSAA49011.2020.00030
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Tags
  • Kernel

  • support vector machin...

  • data visualization

  • optimization

  • Prototypes

  • minimization

  • level set

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