通过聚类与补全互推，提升复杂数据中缺失值的恢复精度。
Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery
- 用动态聚类作为先验，指导生成模型补全数据。
- 在14个数据集上优于15种基线方法，尤其在异质数据中表现更优。
- 适合处理存在多子群结构的真实数据缺失问题。
缺失数据在实际应用中普遍存在，有效补全是下游分析的关键预处理步骤。真实数据常包含多个分布不同的潜在子群，但现有方法常忽略这种异质性，导致补全结果模糊子群边界、缺乏个体精度。虽然引入子群信息可改善结果，却面临循环依赖：可靠聚类需完整数据，而数据补全正是目标。为此，我们提出CAGI（聚类感知生成补全）框架，将聚类与补全重构为相互促进的协同优化过程。CAGI采用‘划分-引导-恢复’策略，利用动态聚类分配作为局部先验，指导生成对抗网络进行补全，并建立迭代反馈环，逐步优化聚类结构与补全值，逼近真实子群分布。为保障分布稳定性，还引入结合实例重建与分布正则化的多层次优化目标。在14个基准数据集和15种代表性基线上的实验表明，CAGI显著优于现有方法，源代码已开源。
原文摘要 · Abstract (English)
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
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