DBMiner: a system for data mining in relational databases and data warehouses
Author(s) -
Jiawei Han,
Jenny Chiang,
Sonny Han Seng Chee,
Jianping Chen,
Qing Chen,
Shan Cheng,
Wan Gong,
Micheline Kamber,
Krzysztof Koperski,
Gang Liu,
Yijun Lu,
Nebojsa Stefanovic,
Lara Winstone,
Betty Xia,
Osmar R. Zaïane,
Shuhua Zhang,
Hua Zhu
Publication year - 1997
Language(s) - English
DOI - 10.1145/782010.782018
A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. By incorporating several interesting data mining techniques, including OLAP and 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.
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