C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation

2026-02-25

AI Summary

This work addresses the high computational cost and neglect of heterogeneous features and class imbalance in existing tabular data condensation methods. We propose the first training-free framework for tabular data condensation, formulating the condensation objective as a class-adaptive clustering assignment problem that jointly optimizes class allocation and feature representation. To efficiently solve this NP-hard problem, we introduce a Hybrid Categorical Feature Encoding (HCFE) scheme coupled with a heuristic local search algorithm (HFILS), leveraging soft assignment and intra-class clustering strategies. Extensive experiments on ten real-world datasets demonstrate that our method achieves at least two orders of magnitude speedup over state-of-the-art approaches while delivering superior performance on downstream tasks.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Data Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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, 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 C$^{2}$TC improves efficiency by at least 2 orders of magnitude over state-of-the-art baselines, while achieving superior downstream performance.

Problem

• tabular data
• dataset condensation
• class imbalance
• heterogeneous features
• data efficiency

Innovation

• training-free
• tabular data condensation
• class-adaptive clustering
• heterogeneous features
• class imbalance