Shells within Minimum Enclosing Balls
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.
- Published in:
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) International Conference on Data Science and Advanced Analytics (DSAA) - Type:
Inproceedings - Authors:
C. Bauckhage, M. Bortz, R. Sifa - Year:
2020
Citation information
C. Bauckhage, M. Bortz, R. Sifa: Shells within Minimum Enclosing Balls, International Conference on Data Science and Advanced Analytics (DSAA), 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, https://doi.org/10.1109/DSAA49011.2020.00030, Bauckhage.etal.2020d,
@Inproceedings{Bauckhage.etal.2020d,
author={C. Bauckhage, M. Bortz, R. Sifa},
title={Shells within Minimum Enclosing Balls},
booktitle={International Conference on Data Science and Advanced Analytics (DSAA)},
journal={2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
url={https://doi.org/10.1109/DSAA49011.2020.00030},
year={2020},
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...}}