CELEUX , Gilles

International audience

Model-based unsupervised learning, as any learning task, stalls as soon asmissing data occurs. This is even more true when the missing data are infor-mative, or said missing not at random (MNAR). In this paper, we proposemodel-based clustering algorithms designed to handle very general typesof missing data, including MNAR data. To do so, we...

Traditional ways for handling missing values are not designed for the clustering purpose and they rarely apply to the general case, though frequent in practice, of Missing Not At Random (MNAR) values. This paper proposes to embed MNAR data directly within model-based clustering algorithms. We introduce a mixture model for different types of...

International audience

Model-based unsupervised learning, as any learning task, stalls as soon as missingdata occurs. This is even more true when the missing data are informative, or saidmissing not at random (MNAR). In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data. To do so, we...

This document is the accompanying note of the main paper "Model-based Clustering with Missing Not At Random Data". We assume the data missing not at random (MNAR) values, i.e. the effect of missingness depends on on the missing values themselves.An example includes clinical data collected in emergency situations, where doctors may choose to...

International audience