# TableDC: Deep Clustering for Tabular Data

arXiv: https://arxiv.org/abs/2405.17723 · HTML: https://mlsanity.com/paper/2405.17723/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2024.

## Built on by (most cited first, 9 of 9)

- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [Unveiling Multiple Descents in Unsupervised Autoencoders](https://mlsanity.com/paper/2406.11703/) (2024)
- [Categorical Data Clustering via Value Order Estimated Distance Metric Learning](https://mlsanity.com/paper/2411.15189/) (2024)
- [TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering](https://mlsanity.com/paper/2601.21656/) (2026)
- [Gem: Gaussian Mixture Model Embeddings for Numerical Feature Distributions](https://mlsanity.com/paper/2410.07485/) (2024)
- [An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter](https://mlsanity.com/paper/2511.17983/) (2025)
- [Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data](https://mlsanity.com/paper/2604.05857/) (2026)
- [Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering](https://mlsanity.com/paper/2604.10865/) (2026)
- [PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering](https://mlsanity.com/paper/2605.08673/) (2026)

## Abstract (arXiv)

Deep clustering (DC), a fusion of deep representation learning and clustering, has recently demonstrated positive results in data science, particularly text processing and computer vision. However, joint optimization of feature learning and data distribution in the multi-dimensional space is domain-specific, so existing DC methods struggle to generalize to other application domains (such as data integration and cleaning). In data management tasks, where high-density embeddings and overlapping clusters dominate, a data management-specific DC algorithm should be able to interact better with the data properties for supporting data cleaning and integration tasks. This paper presents a deep clustering algorithm for tabular data (TableDC) that reflects the properties of data management applications, particularly schema inference, entity resolution, and domain discovery. To address overlapping clusters, TableDC integrates Mahalanobis distance, which considers variance and correlation within the data, offering a similarity method suitable for tables, rows, or columns in high-dimensional latent spaces. TableDC provides flexibility for the final clustering assignment and shows higher tolerance to outliers through its heavy-tailed Cauchy distribution as the similarity kernel. The proposed similarity measure is particularly beneficial where the embeddings of raw data are densely packed and exhibit high degrees of overlap. Data cleaning tasks may involve a large number of clusters, which affects the scalability of existing DC methods. TableDC's self-supervised module efficiently learns data embeddings with a large number of clusters compared to existing benchmarks, which scale in quadratic time. We evaluated TableDC with several existing DC, Standard Clustering (SC), and state-of-the-art bespoke methods over benchmark datasets. TableDC consistently outperforms existing DC, SC, and bespoke methods.