DBLearn: A System Prototype for Knowledge Discovery in Relational Databases
Summary: DBLearn is a prototype data-mining system for relational databases, integrated with commercial RDBMS to extract rules. Interfaces, automatic concept-hierarchy refinement, and fast discovery; research extends to OO, deductive, and spatial DBs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiawei Han
- 2. Yongjian Fu
- 3. Yue Huang
- 4. Yandong Cai
- 5. Nick Cercone
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,539 | General purpose database summarization | 2005 | VLDB | 6.9925094e-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 |
|---|---|---|---|---|
| 146 | Knowledge Discovery in Databases: An Attribute-Oriented Approach | 1992 | VLDB | 0.00041260875 |
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| 14,083 | DataMine - Interactive Rule Discovery System | 1995 | SIGMOD | - |
| 146 | Knowledge Discovery in Databases: An Attribute-Oriented Approach | 1992 | VLDB | 0.00041260875 |
| 14,037 | DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases | 1996 | SIGMOD | - |