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)
Incoming Non-self Citations Over Time
Authors
- 1. Dimitrios Gunopulos
- 2. Roni Khardon
- 3. Heikki Mannila
- 4. Hannu Toivonen
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 | 0.00029356968 |
| 9,062 | Feasible Itemset Distributions in Data Mining: Theory and Application | 2003 | PODS | 4.3997447e-05 |
| 12,628 | How to Quickly Find a Witness | 2003 | PODS | 4.1905499e-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 | 0.0010863639 |
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