DBMiner: interactive mining of multiple-level knowledge in relational databases

Authors:
Jaiwei Han;Youngjian Fu;Wei Wang;Jenny Chiang;Osmar R. Zaïane;Krzysztof Koperski
Affiliations:
Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;Data Mining Research Group, Database Systems Research Laboratory, School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6
Venue:
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Year:
1996

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Abstract

Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.