Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery

2026-07-07

AI Summary

Existing missing value imputation methods often overlook heterogeneous subgroup structures within data, leading to blurred subgroup boundaries and insufficient individual fidelity. This work proposes the CAGI framework, which formulates clustering and imputation as a co-optimization problem for the first time. Through a “partition-guide-recover” strategy, CAGI employs dynamic clustering to condition a generative adversarial network with local priors and introduces an iterative feedback mechanism to jointly refine subgroup structure and imputation results. By integrating instance-level reconstruction with distribution-level regularization objectives, the method achieves subgroup-aware, high-fidelity imputation and significantly outperforms 15 state-of-the-art approaches across 14 benchmark datasets.

Abstract

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

Problem

• missing data imputation
• latent subgroup structure
• population heterogeneity
• cluster-aware imputation
• data recovery

Innovation

• cluster-aware imputation
• missing data recovery
• generative adversarial networks
• latent subgroup structure
• co-optimization