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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)

Paper ID
7342
Venue
SIGMOD
Year
2025
Pagerank
5.6029996e-05
Overall Rank
7,501 | 48.54%
DOI
10.1145/3725366

Incoming Non-self Citations Over Time

Authors

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.

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