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Shells within Minimum Enclosing Balls

: Bauckhage, Christian; Bortz, Michael; Sifa, Rafet


Webb, G. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020. Proceedings : 6-9 October 2020, Sydney, Australia
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2020
ISBN: 978-1-7281-8206-3
ISBN: 978-1-7281-8207-0
International Conference on Data Science and Advanced Analytics (DSAA) <7, 2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01IS18038A; ML2R
Fraunhofer IAIS ()
Fraunhofer ITWM ()
Kernel; support vector machines; data visualization; optimization; Prototypes; minimization; level set

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.