An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture based clustering

TL;DR

This paper presents a new method for selecting the number of clusters in Gaussian mixture models that accounts for noise and assesses cluster quality using a nonparametric measure, improving clustering robustness.

Contribution

It introduces an adequacy-based approach combining a nonparametric quality measure with model simplicity criteria, applicable to OTRIMLE and other clustering methods.

Findings

The proposed method effectively determines the number of clusters in simulations.

It compares favorably with BIC and ICL criteria in real datasets.

The approach handles noise and non-Gaussian clusters well.

Abstract

We introduce a new approach to deciding the number of clusters. The approach is applied to Optimally Tuned Robust Improper Maximum Likelihood Estimation (OTRIMLE; Coretto and Hennig 2016) of a Gaussian mixture model allowing for observations to be classified as "noise", but it can be applied to other clustering methods as well. The quality of a clustering is assessed by a statistic $Q$ that measures how close the within-cluster distributions are to elliptical unimodal distributions that have the only mode in the mean. This nonparametric measure allows for non-Gaussian clusters as long as they have a good quality according to $Q$. The simplicity of a model is assessed by a measure $S$ that prefers a smaller number of clusters unless additional clusters can reduce the estimated noise proportion substantially. The simplest model is then chosen that is adequate for the data in the sense that its observed value of $Q$ is not significantly larger than what is expected for data truly generated from the fitted model, as can be assessed by parametric bootstrap. The approach is compared with model-based clustering using the Bayesian Information Criterion (BIC) and the Integrated Complete Likelihood (ICL) in a simulation study and on two datasets of scientific interest. Keywords: parametric bootstrap; noise component; unimodality; model-based clustering