DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases

Summary: DBMiner enables interactive multi-level knowledge mining in relational DBs. Offers generalization, characterization, association, and prediction; uses attribute-oriented induction, progressive deepening for multi-level rules, and meta-rule guided mining. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 2984
Venue: SIGMOD
Year: 1996
Pagerank: -
Overall Rank: 14,222 | 2.43%
DOI: 10.1145/233269.280356

Incoming Non-self Citations Over Time

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Authors

1. Jiawei Han (Simon Fraser University)
2. Yongjian Fu (Simon Fraser University)
3. Wei Wang (Simon Fraser University)
4. Jenny Chiang (Simon Fraser University)
5. Osmar R. Zaiane (Simon Fraser University)
6. Krzysztof Koperski (Simon Fraser University)

BibTeX Citation

@inproceedings{han_sigmod96,
        title = {{DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases}},
        author = {Han, Jiawei and Fu, Yongjian and Wang, Wei and Chiang, Jenny and Zaiane, Osmar R. and Koperski, Krzysztof},
        series = {{SIGMOD} '96},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/233269.280356},
        url = {https://dl.acm.org/doi/10.1145/233269.280356},
        year = {1996}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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
456	Discovery of Multiple-Level Association Rules from Large Databases	1995	VLDB	0.00018124452

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