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

Jiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Osmar R. Zaiane, Krzysztof Koperski

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)550
Number of pages1
JournalSIGMOD Record (ACM Special Interest Group on Management of Data)
Volume25
Issue number2
DOIs
StatePublished - 1996
Externally publishedYes
EventProceedings of the 1996 ACM SIGMOD International Conference on Management of Data - Montreal, Can
Duration: Jun 4 1996Jun 6 1996

ASJC Scopus subject areas

  • Software
  • Information Systems

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