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Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases

Summary: Mining association rules for nearly optimal binary segmentations of huge categorical databases. Convex objective functions (entropy, chi-square, Gini) yield feasible near-optimal splits; practical algorithms and computational-geometry techniques handle non-binary targets where traditional methods fail. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8504
Venue
VLDB
Year
1998
Pagerank
6.0261932e-05
Overall Rank
4,643 | 67.71%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
1,455 RainForest - A Framework for Fast Decision Tree Construction of Large Datasets 1998 VLDB 0.00011899821
2,687 BOAT—Optimistic Decision Tree Construction 1999 SIGMOD 8.3050259e-05
3,454 Traversing Itemset Lattices with Statistical Metric Pruning 2000 PODS 7.0778482e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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