Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering

• URL: http://arxiv.org/abs/2604.10865v1
• Date: Mon, 13 Apr 2026 00:25:22 GMT
• Title: Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
• Abstract summary: This paper proposes Tabular-Augmented Contrastive Clustering (TagCC) to bridge the gap between dataset-specific statistics and intrinsic semantic knowledge.<n>TagCC distills underlying data semantics into textual anchors via semantic-aware transformation.<n>It is jointly optimized with a clustering objective, ensuring that the learned representations are both semantically coherent and clustering-friendly.
• Score: 52.97486694817375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level statistical co-occurrence to infer the latent metric space, often overlooking the intrinsic semantic knowledge encapsulated in feature names and values. As a result, semantically related concepts like `Flu' and `Cold' are often treated as symbolic tokens, causing conceptually related samples to be isolated. To bridge the gap between dataset-specific statistics and intrinsic semantic knowledge, this paper proposes Tabular-Augmented Contrastive Clustering (TagCC), a novel framework that anchors statistical tabular representations to open-world textual concepts. Specifically, TagCC utilizes Large Language Models (LLMs) to distill underlying data semantics into textual anchors via semantic-aware transformation. Through Contrastive Learning (CL), the framework enriches the statistical tabular representations with the open-world semantics encapsulated in these anchors. This CL framework is jointly optimized with a clustering objective, ensuring that the learned representations are both semantically coherent and clustering-friendly. Extensive experiments on benchmark datasets demonstrate that TagCC significantly outperforms its counterparts.

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