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Data mining, Hypergraph Transversals, and Machine Learning

Summary: Maps discovery of maximally-specific “interesting” sentences in databases to the hypergraph-transversal problem, enabling formal complexity analysis. Analyzes two algorithms—one efficient for small patterns (improves a special-case transversal), the other uses transversal subroutines with near-optimal bounds and ties results to exact learning. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1118
Venue
PODS
Year
1997
Pagerank
5.0530117e-05
Overall Rank
6,465 | 55.03%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
277 Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications 1998 SIGMOD 0.00029311426
9,064 Feasible Itemset Distributions in Data Mining: Theory and Application 2003 PODS 4.4039656e-05
12,619 How to Quickly Find a Witness 2003 PODS 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0010864752
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