TY - JOUR
T1 - Advances of the DBLearn System for Knowledge Discovery in Large Databases
AU - Han, Jiawei
AU - Fu, Yongjian
AU - Tang, Simon
N1 - With the rapid growth of the number of databases and the tremendous amounts of data being collected and stored in databases, it is increasingly important to develop software tools for data mining or knowledge discovery in databases [Piatetsky-Shapiro and Frawley, 1991; Fayyad et ai, 1995]. Data mining is the extraction of "information" or "knowledge" from data, which helps understanding data in databases and automatic construction of knowledgebases from databases. DBLearn is such a knowledge discovery system prototype, developed in Simon Fraser University between 1989 and 1993 [Cai et a/., 1991; Han et a/., 1993; 1994]. It discovers characteristic rules and discriminant rules embedded in relational databases. The major features of the system are speed and efficiency in analyzing large databases, interactive knowledge mining, and smooth integration with commercial relational database systems. Experiments with DBLearn have been performed in NSERC (Natural Science and Engineering * Research is partially supported by the Natural Sciences and Engineering Research Council of Canada under the grant OGP0037230, by the Networks of Centres of Excellence Program (with the participation of PRECARN association) under the grant IRIS:HMI-5, and by a research grant from the Hughes Research Laboratories.
PY - 1995
Y1 - 1995
N2 - A prototyped data mining system, DBLearn, was developed in Simon Fraser Univ., which integrates machine learning methodologies with database technologies and efficiently and effectively extracts characteristic and discriminant rules from relational databases. Further developments, of DBLearn lead to a new generation data mining system: DBMiner, with the following features: (1) mining new kinds of rules from large databases, including multiple-level association rules, classification rules, cluster description rules, etc., (2) automatic generation and refinement of concept hierarchies, (3) high level SQL-like and graphical data mining interfaces, and (4) client/server architecture and performance improvements for large applications. The major features of the system are demonstrated with experiments in a research grant information database.
AB - A prototyped data mining system, DBLearn, was developed in Simon Fraser Univ., which integrates machine learning methodologies with database technologies and efficiently and effectively extracts characteristic and discriminant rules from relational databases. Further developments, of DBLearn lead to a new generation data mining system: DBMiner, with the following features: (1) mining new kinds of rules from large databases, including multiple-level association rules, classification rules, cluster description rules, etc., (2) automatic generation and refinement of concept hierarchies, (3) high level SQL-like and graphical data mining interfaces, and (4) client/server architecture and performance improvements for large applications. The major features of the system are demonstrated with experiments in a research grant information database.
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M3 - Conference article
AN - SCOPUS:0342932924
SN - 1045-0823
VL - 2
SP - 2049
EP - 2050
JO - IJCAI International Joint Conference on Artificial Intelligence
JF - IJCAI International Joint Conference on Artificial Intelligence
T2 - 14th International Joint Conference on Artificial Intelligence, IJCAI 1995
Y2 - 20 August 1995 through 25 August 1995
ER -