Shells within Minimum Enclosing Balls

Author: C. Bauckhage, M. Bortz, R. Sifa
Journal: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2020

Citation information

C. Bauckhage, M. Bortz, R. Sifa,
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA),
2020,
178-187,
IEEE,
Sydney, NSW, Australia,
https://doi.org/10.1109/DSAA49011.2020.00030

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.