DBLearn: a system prototype for knowledge discovery in relational databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Data Mining in Large Databases Using Domain Generalization Graphs
Journal of Intelligent Information Systems
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
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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.