Intelligent　Information　Management　and　Knowledge　Discovery　in　Large　Numeric　and　Scientific　Databases

作     者：Patrick Perrin Frederick E. Petry & William Thomason(Center for Intelligent and Knowledge-Based Systems)(Computer Science Department, Tulane University, New Orleans LA) 

作者机构：Center for Intelligent and Knowledge-Based Systems Computer Science Department Tulane University New Orleans LA 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：1996年第7卷第2期

页      面：73-86页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Knowledge discovery in databases Machine learning Decision tree inducers 

摘      要：The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.