Work

Choice of Trimming Proportion and Number of Clusters in Robust Clustering based on Trimming

Year: 2025

Type: article

Abstract: So-called “classification trimmed likelihood curves” have been proposed as a 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.

Source: Journal of Computational and Graphical Statistics

DOI: 10.1080/10618600.2025.2554675

Authors: Luis Angel García-Escudero, Christian Hennig, Agustín Mayo-Iscar, Gianluca Morelli, Marco Riani

Institutions: Universidad de Valladolid, University of Bologna, University of Parma

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Università degli Studi di Parma, Ministerio de Ciencia e Innovación

Awards: G1995173037, G5184523093