Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database Technologies
- Jiawei Han
- Yongjian Fu
- Krzysztof Koperski
- Gabor Melli
- Wei Wang
- Osmar R. Zaiane, University of Alberta (Database)
Active research has been conducted on knowledge discovery in databases by the researchers in our group for years, with many interesting results published and a prototyped knowledge discovery system, DBMiner (previously called DBLearn), developed and demonstrated in several conferences. Our research covers a wide spectrum of knowledge discovery, including (1) the study of knowledge discovery in relational, object-oriented, deductive, spatial, and active databases, and global information systems, and (2) the development of various kinds of knowledge discovery methods, including attribute-oriented induction, progressive deepening for mining multiple-level rules, meta-rule guided knowledge mining, etc. Techniques for the discovery of various kinds of knowledge, including generalization, characterization, discrimination, association, classi fication, clustering, etc. and the application of knowledge discovery for intelligent query answering, multiple-layered database construction, etc. have also been studied in our research.
Citation
J. Han, Y. Fu, K. Koperski, G. Melli, W. Wang, O. Zaiane. "Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database Technologies". In Magazine, Canadian AI Magazine, 38(Winter), January 1996.| Keywords: | |
| Category: | In Magazine |
| Web Links: | CAI |
BibTeX
@misc{Han+al:96,
author = {Jiawei Han and Yongjian Fu and Krzysztof Koperski and Gabor Melli
and Wei Wang and Osmar R. Zaiane},
title = {Knowledge Mining in Databases: An Integration of Machine Learning
Methodologies with Database Technologies},
Volume = "38",
Number = "Winter",
year = 1996,
}Last Updated: February 05, 2020Submitted by Sabina P
