C2TC: A Training-Free Framework for Efficient Tabular Data Condensation

Tabular data, organized in rows and columns, represents the most common data format in industrial relational databases, underpinning modern data analytics and decisionmaking. However, the ever-increasing scale of tabular data poses significant computational and storage challenges to learningbased analytical systems. This highlights the need for dataefficient learning, which maximizes the utility of available data to enable effective model training and generalization using substantially fewer samples. Dataset condensation (DC) has recently emerged as a promising data-centric paradigm that synthesizes small yet informative datasets to preserve data utility while greatly reducing storage and training costs. However, existing DC methods are computationally intensive due to reliance on complex gradient-based optimization. Moreover, they often overlook key characteristics of tabular data, such as heterogeneous features and class imbalance. To address these limitations, we introduce $\mathbf{C}^{2} \mathbf{T C}$ (Class-Adaptive Clustering for Tabular Condensation), the first training-free tabular dataset condensation framework that jointly optimizes class allocation and feature representation, enabling efficient and scalable condensation. Specifically, we reformulate the dataset condensation objective into a novel class-adaptive cluster allocation problem (CCAP), which eliminates costly training and integrates adaptive label allocation to handle class imbalance. To solve the NP-hard CCAP, we develop HFILS, a heuristic local search that alternates between soft allocation and class-wise clustering to efficiently obtain high-quality solutions. Moreover, a hybrid categorical feature encoding (HCFE) is proposed for semantics-preserving clustering of heterogeneous discrete attributes. Extensive experiments on 10 real-world datasets demonstrate that $\mathbf{C}^{\mathbf{2}} \mathbf{T C}$ improves efficiency by at least 2 orders of magnitude over state-of-the-art baselines, while achieving superior downstream performance.