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This paper introduces CAGI (Cluster-Aware Generative Imputation), a novel framework that integrates clustering and imputation into a co-optimization process to address the challenges of missing data in heterogeneous populations. By utilizing a "Partition-Guide-Restore" strategy, CAGI dynamically assigns cluster memberships to inform a Generative Adversarial Network, allowing for iterative refinement of both cluster structures and imputed values. Extensive experiments across 14 benchmark datasets reveal that CAGI significantly outperforms 15 existing imputation methods, demonstrating its effectiveness in preserving subgroup distributions and enhancing instance-level fidelity.
CAGI achieves superior imputation accuracy by leveraging latent subgroup structures, outperforming traditional methods that ignore population heterogeneity.
Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis. Real-world datasets often exhibit complex latent structures composed of multiple subgroups with distinct distributions. However, existing methods often overlook such population heterogeneity. Without explicit structural guidance, these methods tend to produce generic estimates that blur subgroup boundaries and lack instance-level fidelity. While incorporating subgroup information offers a remedy, it faces a circular dependency: reliable subgroup identification requires complete data, while data completion is the imputation objective itself. To resolve this, we propose CAGI (Cluster-Aware Generative Imputation), a framework that reformulates clustering and imputation as a mutually reinforcing co-optimization process. CAGI employs a ``Partition-Guide-Restore''strategy where dynamic cluster assignments act as local priors to condition a Generative Adversarial Network. An iterative feedback loop is established to progressively refine both cluster structures and imputed values toward faithful subgroup distributions. To ensure distributional stability, CAGI further employs a multi-level optimization objective combining instance-level reconstruction with distribution-level regularization. Extensive experiments on 14 benchmark datasets with 15 representative baselines demonstrate the superiority of CAGI. The source code is available at: https://github.com/supercocachii/CAGI
