Computer Science > Artificial Intelligence
arXiv:2606.27061 (cs)
[Submitted on 25 Jun 2026]
Abstract:External indexes can be used for cluster evaluation when ground truth is available. We review the most common external validity indexes focusing on set-matching-based measures. We recommend centroid index (CI), because it is an intuitive cluster-level measure with an explainable result. If we need a more fine-tuned, point-level measure, there are more choices. Pair-set index (PSI) provides a normalized score which is not biased by cluster sizes. If all points should matter equally, then clustering accuracy (ACC) or any other set-matching measure is suitable.
| Comments: | Preprint of a book chapter to appear: P. Fränti, "How to evaluate clustering with ground truth?", In Center-based clustering, Springer Nature, 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.27061 [cs.AI] |
| (or arXiv:2606.27061v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27061 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pasi Fränti [view email]
[v1]
Thu, 25 Jun 2026 14:07:17 UTC (1,554 KB)
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