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Incomplete clustering analysis via multiple imputation

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  • Jung Wun Lee
  • Ofer Harel

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.

Suggested Citation

  • Jung Wun Lee & Ofer Harel, 2023. "Incomplete clustering analysis via multiple imputation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(9), pages 1962-1979, July.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1962-1979
    DOI: 10.1080/02664763.2022.2060952
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