Clustering with missing data: which equivalent for Rubin's rules?
This work provides a comprehensive framework for researchers and practitioners performing clustering analysis on datasets with missing values, offering improved accuracy and enhanced instability assessment.
This paper addresses the challenge of clustering with missing data by proposing a complete framework using multiple imputation (MI). It tackles how to pool partitions using consensus clustering and how to assess clustering instability with incomplete data, showing that pooling improves accuracy and instability assessment expands data analysis possibilities.
Multiple imputation (MI) is a popular method for dealing with missing values. However, the suitable way for applying clustering after MI remains unclear: how to pool partitions? How to assess the clustering instability when data are incomplete? By answering both questions, this paper proposed a complete view of clustering with missing data using MI. The problem of partitions pooling is here addressed using consensus clustering while, based on the bootstrap theory, we explain how to assess the instability related to observed and missing data. The new rules for pooling partitions and instability assessment are theoretically argued and extensively studied by simulation. Partitions pooling improves accuracy, while measuring instability with missing data enlarges the data analysis possibilities: it allows assessment of the dependence of the clustering to the imputation model, as well as a convenient way for choosing the number of clusters when data are incomplete, as illustrated on a real data set.