On the Consistency of Metric and Non-Metric K-medoids
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Abstract (arXiv)
We establish the consistency of K-medoids in the context of metric spaces. We start by proving that K-medoids is asymptotically equivalent to K-means restricted to the support of the underlying distribution under general conditions, including a wide selection of loss functions. This asymptotic equivalence, in turn, enables us to apply the work of Parna (1986) on the consistency of K-means. This general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available. We provide some numerical experiments to illustrate our theory.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2022Asymptotic Theory of Geometric and Adaptive k-Means Clusteringcited by 4
- 2025Consistency and inconsistency in k-means clusteringcited by 0
- 2026The Condition-Number Principle for Prototype Clusteringcited by 0
- 2026The Condition-Number Principle for Prototype Clustering9 Apr 2026
- 2025Consistency and inconsistency in k-means clustering8 Jul 2025
- 2022Asymptotic Theory of Geometric and Adaptive k-Means Clustering27 Feb 2022