Choice of trimming proportion and number of clusters in robust clustering based on trimming

2023/09/15 by Luis Ángel García-Escudero, García-Escudero, Luis Angel, Christian Hennig +7
Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Face and Expression Recognition #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2309.08468

openalex publication_date 2023/09/15 · openalex created_date 2023/09/19 · openalex updated_date 2026/07/28

Abstract

So-called "classification trimmed likelihood curves" have been proposed as a useful heuristic tool to determine the number of clusters and trimming proportion in trimming-based robust clustering methods. However, these curves needs a careful visual inspection, and this way of choosing parameters requires subjective decisions. This work is intended to provide theoretical background for the understanding of these curves and the elements involved in their derivation. Moreover, a parametric bootstrap approach is presented in order to automatize the choice of parameter more by providing a reduced list of "sensible" choices for the parameters. The user can then pick a solution that fits their aims from that reduced list.

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