How can we theoretically measure the performance of density-based clustering algorithms?
Many of clustering algorithms for a point cloud X n ⊂ ℝ d in the Euclidean space are based on density estimates [1]. In fact, the density function f of point generation contains the relevant information. It is quite natural to try to extract what Hartigan called 'high-density clusters' [2].
Authors
- Louis Hauseux (ORCID: https://orcid.org/0009-0007-3570-746X)
Institutions
- Institut national de recherche en sciences et technologies du numérique (FR)
- Université Côte d'Azur (FR)
- Institut de Biologie Valrose (FR)
Publication Details
- Journal
- ACM SIGMETRICS Performance Evaluation Review
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1145/3725536.3725546
- Primary Topic
- Advanced Clustering Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00