Categorical Data Clustering via Value Order Estimated Distance Metric Learning
Summary: Introduces an order-distance metric that learns optimal ordinal relationships among categorical values by embedding them on a line to induce Euclidean-like distances for clustering. Proposes an alternating joint clustering–metric-learning algorithm with convergence and low cost, improving accuracy and interpretability on categorical and mixed data. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Yiqun Zhang (Guangdong University of Technology; Hong Kong Baptist University)
- 2. Mingjie Zhao (Hong Kong Baptist University)
- 3. Hong Jia (Shenzhen University)
- 4. Mengke Li (Shenzhen University)
- 5. Yang Lu (Xiamen University)
- 6. Yiu-ming Cheung (Hong Kong Baptist University)
BibTeX Citation
@inproceedings{zhang_sigmod26,
title = {{Categorical Data Clustering via Value Order Estimated Distance Metric Learning}},
author = {Zhang, Yiqun and Zhao, Mingjie and Jia, Hong and Li, Mengke and Lu, Yang and Cheung, Yiu-ming},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769772},
url = {https://dl.acm.org/doi/10.1145/3769772},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,713 | Clustering Categorical Data: An Approach Based on Dynamical Systems | 1998 | VLDB | 9.94811e-05 |
| 5,960 | On Graph Representation for Attributed Hypergraph Clustering | 2025 | SIGMOD | 6.0274692e-05 |
| 7,501 | TableDC: Deep Clustering for Tabular Data | 2025 | SIGMOD | 5.6029996e-05 |
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