Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
Summary: arXiv:2604.10865v1 Announce Type: new
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.
The Novel Framework: TagCC
TagCC utilizes Large Language Models (LLMs) to distill underlying data semantics into textual anchors via semantic-aware transformation. This innovative approach allows the framework to better understand and utilize the relationships between features in tabular data, enhancing the clustering process.
Mechanism of Action
Through Contrastive Learning (CL), TagCC enriches the statistical tabular representations with the open-world semantics encapsulated in these anchors. The integration of CL helps in differentiating and associating similar samples more effectively.
Optimization Process
This CL framework is jointly optimized with a clustering objective, ensuring that the learned representations are both semantically coherent and clustering-friendly. This dual optimization allows TagCC to learn not only the inherent statistical properties of the data but also the semantic relationships that exist within the feature space.
Performance Evaluation
Extensive experiments on benchmark datasets demonstrate that TagCC significantly outperforms its counterparts. The results showcase that by integrating intrinsic semantic knowledge into the clustering process, TagCC is able to group related data points more effectively than traditional methods.
Key Advantages of TagCC
- Enhanced Understanding: Utilizes semantic knowledge from feature names and values.
- Improved Clustering: Groups related samples more effectively by overcoming limitations of traditional methods.
- Robust Framework: Combines statistical analysis with semantic insights for a more comprehensive approach to data clustering.
- Real-World Application: Particularly beneficial in domains like finance and healthcare where data interpretation is critical.
Conclusion
The introduction of TagCC marks a significant advancement in the field of deep clustering for tabular data. By harnessing the power of Large Language Models and semantic-aware transformations, this framework not only enhances the understanding of data but also improves the clustering process, paving the way for more insightful analyses in various real-world applications.