ArticleJournal of applied statistics2023
Incomplete clustering analysis via multiple imputation.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A comparative simulation study of cluster ensemble algorithms integrated with multiple imputation for clustering with missing data.BMC medical research methodology · 2026Article
- A latent class pattern mixture model for nonignorable nonresponses in multivariate categorical data.Computational statistics · 2025Article
- Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts.Psychometrika · 2025Article
- Identification of Acute Respiratory Failure Phenotypes With Electronic Health Record Data.CHEST critical care · 2025Article
- Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership.Journal of cancer survivorship : research and practice · 2025Article
- Sleep trajectories and osteoporosis incidence: findings from two prospective cohort studies.Frontiers in public health · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.
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Registered trials
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