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)

Paper ID: 7368
Venue: SIGMOD
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,060 | 30.02%
DOI: 10.1145/3769772

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,336	Clustering Categorical Data: An Approach Based on Dynamical Systems	1998	VLDB	0.00012498064
5,827	On Graph Representation for Attributed Hypergraph Clustering	2025	SIGMOD	5.3113542e-05
6,894	TableDC: Deep Clustering for Tabular Data	2025	SIGMOD	4.8925595e-05

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