Advances of the DBLearn System for Knowledge Discovery in Large Databases

Jiawei Han, Yongjian Fu, Simon Tang

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2049-2050
Number of pages2
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 1995
Externally publishedYes
Event14th International Joint Conference on Artificial Intelligence, IJCAI 1995 - Montreal, Canada
Duration: Aug 20 1995Aug 25 1995

ASJC Scopus subject areas

  • Artificial Intelligence


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