Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DSAA49011.2020.00030 | 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | DocType | ISSN |
data clustering,data set,kernel minimum enclosing ball,nonnumeric data,graph-data,abstract feature spaces,text-data,image-data | Conference | 2472-1573 |
ISBN | Citations | PageRank |
978-1-7281-8207-0 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Bauckhage | 1 | 1979 | 195.86 |
| Michael Bortz | 2 | 0 | 0.34 |
| Rafet Sifa | 3 | 133 | 30.03 |