DBMiner: A System for Mining Knowledge in Large Relational Databases

Jiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Wan Gong, Krzysztof Koperski, Deyi Li, Yijun Lu, Amynmohamed Rajan, Nebojsa Stefanovic, Betty Xia, Osmar R. Zaiane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A data mining system, DBMiner, 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 incorporating several interesting data mining techniques, including attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996
EditorsEvangelos Simoudis, Jiawei Han, Usama M. Fayyad
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Pages250-255
Number of pages6
ISBN (Electronic)1577350049, 9781577350040
StatePublished - 1996
Externally publishedYes
Event2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996 - Portland, United States
Duration: Aug 2 1996Aug 4 1996

Publication series

NameProceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Conference

Conference2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996
Country/TerritoryUnited States
CityPortland
Period8/2/968/4/96

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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