CAGI Improves Missing Data Imputation with Cluster-Aware Generative Approach

Missing data is a common challenge in real-world datasets, making effective imputation a critical preprocessing step. Many existing imputation methods, however, overlook the inherent heterogeneity within data, where multiple subgroups might have distinct distributions. This oversight can lead to generic estimates that blur subgroup boundaries and lack accuracy at the individual instance level. The challenge lies in a circular dependency: reliable subgroup identification requires complete data, but data completion is the goal of imputation itself. To break this cycle, researchers propose CAGI (Cluster-Aware Generative Imputation). CAGI reframes clustering and imputation as a mutually reinforcing co-optimization process. It employs a "Partition-Guide-Restore" strategy where dynamic cluster assignments serve as local priors to guide a Generative Adversarial Network. An iterative feedback loop continuously refines both the cluster structures and the imputed values, ensuring they align with faithful subgroup distributions. Extensive experiments demonstrate CAGI's superior performance on various benchmark datasets.