TableDC: Deep Clustering for Tabular Data
Summary: TableDC applies deep clustering to tabular data, learning embeddings for schemas, rows, and domains. It blends Mahalanobis distance with a heavy-tailed Cauchy kernel to handle overlap/outliers and scales to many clusters for data integration. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hafiz Tayyab Rauf (University of Manchester)
- 2. André Freitas (IDIAP Research Institute; University of Manchester)
- 3. Norman W. Paton (University of Manchester)
BibTeX Citation
@inproceedings{rauf_sigmod25,
title = {{TableDC: Deep Clustering for Tabular Data}},
author = {Rauf, Hafiz Tayyab and Freitas, André and Paton, Norman W.},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725366},
url = {https://dl.acm.org/doi/10.1145/3725366},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,352 | Categorical Data Clustering via Value Order Estimated Distance Metric Learning | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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