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[2602.21717] C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation
Summary
C$^{2}$TC introduces a training-free framework for efficient tabular data condensation, addressing challenges in data scalability and model training costs.
Why It Matters
As tabular data becomes increasingly prevalent in analytics, efficient data handling is crucial. C$^{2}$TC offers a novel approach to dataset condensation that significantly reduces computational demands while improving performance, making it relevant for data scientists and machine learning practitioners seeking efficient solutions.
Key Takeaways
- C$^{2}$TC is a training-free framework that optimizes tabular data condensation.
- It addresses class imbalance and heterogeneous features in tabular datasets.
- The method improves efficiency by at least 2 orders of magnitude compared to existing techniques.
- A novel heuristic local search algorithm is introduced for optimal class allocation.
- Extensive experiments validate the framework's superior performance on real-world datasets.
Computer Science > Machine Learning arXiv:2602.21717 (cs) [Submitted on 25 Feb 2026] Title:C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation Authors:Sijia Xu, Fan Li, Xiaoyang Wang, Zhengyi Yang, Xuemin Lin View a PDF of the paper titled C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation, by Sijia Xu and 4 other authors View PDF HTML (experimental) Abstract:Tabular data is the primary data format in industrial relational databases, underpinning modern data analytics and decision-making. However, the increasing scale of tabular data poses significant computational and storage challenges to learning-based analytical systems. This highlights the need for data-efficient learning, which enables effective model training and generalization using substantially fewer samples. Dataset condensation (DC) has emerged as a promising data-centric paradigm that synthesizes small yet informative datasets to preserve data utility while 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 C$^{2}$TC (Class-Adaptive Clustering for Tabular Condensation), the first training-free tabular dataset condensation framework that jointly optimizes class allocation and feature representation, ena...
