How to evaluate clustering with ground truth?

Pasi Fränti
6/25/2026
cs.AI

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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.

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Cite this paper

@article{frnti2026evaluate,
  title  = {How to evaluate clustering with ground truth?},
  author = {Pasi Fränti},
  year   = {2026},
  eprint = {2606.27061},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.27061v1}
}

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