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Clustering Categorical Data: An Approach Based on Dynamical Systems

Summary: Clustering unordered categorical data via iterative weight propagation on values, yielding a co-occurrence–based similarity; framed as a nonlinear dynamical system. Experiments on synthetic and real data show rapid convergence to category correlations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8498
Venue
VLDB
Year
1998
Pagerank
0.00012498064
Overall Rank
1,336 | 90.71%
DOI
-

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