discoCVI: Implementation of the DISCO Metric for Internal Clustering
Evaluation
Implementation of the DISCO (Density-based Internal Score for
Clusterings with nOise) metric, a cluster validity index for evaluating
density-based clustering results without ground truth labels. DISCO is the
first index to explicitly assess the quality of noise point assignments in
addition to cluster quality. It uses density-connectivity distance derived
from a minimum spanning tree of the mutual-reachability graph, providing
interpretable, bounded scores in [-1, 1]. Higher scores indicate better
clustering. Based on Beer et al. (2025)
<doi:10.48550/arXiv.2503.00127>.
| Version: |
0.2.1 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
FNN, Rcpp, stats |
| LinkingTo: |
Rcpp |
| Suggests: |
dbscan, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-10-28 |
| DOI: |
10.32614/CRAN.package.discoCVI |
| Author: |
Amin Entezari 
[aut, cre],
Davide Chicco
[ctb] (Supervision and guidance),
Anna Beer [aut] (Original Python implementation (arXiv:2503.00127)),
Lena Krieger [aut] (Original Python implementation (arXiv:2503.00127)),
Pascal Weber [aut] (Original Python implementation (arXiv:2503.00127)) |
| Maintainer: |
Amin Entezari <amin_entezari at outlook.com> |
| BugReports: |
https://github.com/aminentezari/discoCVI/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/aminentezari/discoCVI |
| NeedsCompilation: |
yes |
| Language: |
en-GB |
| Materials: |
README, NEWS |
| CRAN checks: |
discoCVI results |
Documentation:
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