TARImpute: Task-Aware Auto-Recommender System for Missing Value Imputation Algorithms with Clustering Case Studies

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Abstract

Missing data prevalent in information systems impacts data diversity and fidelity, which systematically degrade clustering performance through biased similarity measures and unstable cluster boundaries. Current large-scale environments lack standardized imputation-clustering pipelines, as existing methods operate independently of downstream tasks without analyzing error propagation effects, leading to unreliable results. To address this, we propose TARImpute , a T ask - A ware auto - R ecommender system for missing value imput ation for clustering. It owns three integrated features: Imputation Impact Profiler for quantitative evaluation of imputation-clustering interactions, Error Propagation Interpreter enabling explainable modeling of imputation error diffusion, and Adaptive Strategy Optimizer for dynamic selection of optimal imputation methods. TARImpute provides state-ofthe-art imputation methods to evaluate their effects on clustering tasks. TARImpute also provides robust, interpretable solutions for low-quality data and shows extensibility to other analytical tasks.