DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases
- Jiawei Han
- Yongjian Fu
- Wei Wang
- Jenny Chiang
- Osmar R. Zaiane, University of Alberta (Database)
- Krzysztof Koperski
Based on our years-of-research, 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 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.
Citation
J. Han, Y. Fu, W. Wang, J. Chiang, O. Zaiane, K. Koperski. "DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases". ACM-SIGMOD International Conference on Management of Data (SIGMOD), Montreal, Canada, (ed: Jennifer Widom), pp 550, July 1996.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{Han+al:(SIGMOD)96,
author = {Jiawei Han and Yongjian Fu and Wei Wang and Jenny Chiang and Osmar
R. Zaiane and Krzysztof Koperski},
title = {DBMiner: Interactive Mining of Multiple-Level Knowledge in
Relational Databases},
Editor = {Jennifer Widom},
Pages = {550},
booktitle = {ACM-SIGMOD International Conference on Management of Data
(SIGMOD)},
year = 1996,
}Last Updated: March 04, 2020Submitted by Sabina P
