Aggregation and maintenance for database mining

Database mining must capture the dynamics of data in the real-world. In other words, mining models would be able to maintain all changed rules when a database is updated. This paper presents a new model of aggregating association rules aimed at not only maintaining association rules in dynamical databases, but also aggregating association rules from di€erent data sources. Indeed, this aggregation of association rules is useful for making decisions. Our experiments show that this model is ecient to aggregate rules from both of dynamical databases and di€erent data sources. Ó 1999 Elsevier Science B.V. All rights reserved.