Clustering with missing data: which equivalent for Rubin's rules?

DOI10.1007/s11634-022-00519-1arXiv2011.13694OpenAlexW3107080025MaRDI QIDQ6050754

Vincent Audigier, Ndeye Niang

Publication date: 19 September 2023

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2011.13694

zbMATH Keywords

uncertaintyclusteringmissing datamultiple imputationconsensus clusteringRubin's rules

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Related Items (1)

Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models

Cites Work

• Handling missing values with regularized iterative multiple correspondence analysis
• Selection of the number of clusters via the bootstrap method
• Cluster-wise assessment of cluster stability
• Recursive partitioning for missing data imputation in the presence of interaction effects
• Consistent selection of the number of clusters via crossvalidation
• Finding Groups in Data
• Inference and missing data
• 10.1162/153244303321897735
• Flexible Imputation of Missing Data, Second Edition
• Data Integration in the Life Sciences
• Clustering multiply imputed multivariate high‐dimensional longitudinal profiles
• k-POD: A Method for k-Means Clustering of Missing Data
• Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures
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