Selecting an optimal clustering solutions is a difficult problem, and there exist many data-driven validation strategies to perform this task. In this paper, we focus on a recent proposal, the BQH and BQS criteria, based on quadratic discriminant scores and bootstrap resampling. We provide more insight on these criteria, comparing them with a likelihood-based alternative and using different resampling schemes.
Empyrical Analysis of the Quadratic Scoring for Selecting Clustering Solutions / Coraggio, Luca; Coretto, Pietro. - (2023), pp. 398-401. (Intervento presentato al convegno CLADAG 2023 tenutosi a Salerno nel 11/09/2023 - 13/09/2023).
Empyrical Analysis of the Quadratic Scoring for Selecting Clustering Solutions
Luca Coraggio
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2023
Abstract
Selecting an optimal clustering solutions is a difficult problem, and there exist many data-driven validation strategies to perform this task. In this paper, we focus on a recent proposal, the BQH and BQS criteria, based on quadratic discriminant scores and bootstrap resampling. We provide more insight on these criteria, comparing them with a likelihood-based alternative and using different resampling schemes.| File | Dimensione | Formato | |
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Coraggio2023b - Empirical Analysis of the Quadratic Scoring for Selecting Clustering Solutions.pdf
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