Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Quest: a project on database mining
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
Materialized views and data warehouses
ACM SIGMOD Record
Materialized views: techniques, implementations, and applications
Materialized views: techniques, implementations, and applications
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Materialized Data Mining Views
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Incremental association rule mining using materialized data mining views
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
SQL-like language for database mining
ADBIS'97 Proceedings of the First East-European conference on Advances in Databases and Information systems
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Data mining is an iterative process. Users issue series of similar data mining queries, in each consecutive run slightly modifying either the definition of the mined dataset, or the parameters of the mining algorithm. This model of processing is most suitable for incremental mining algorithms that reuse the results of previous queries when answering a given query. Incremental mining algorithms require the results of previous queries to be available. One way to preserve those results is to use materialized data mining views. Materialized data mining views store the mined patterns and refresh them as the underlying data change. Data mining and knowledge discovery often take place in a data warehouse environment. There can be many relatively small materialized data mining views defined over the data warehouse. Separate refresh of each materialized view can be expensive, if the refresh process has to re-discover patterns in the original database. In this paper we present a novel approach to materialized data mining view refresh process. We show that the concurrent on-line refresh of a set of materialized data mining views is more efficient than the sequential refresh of individual views. We present the framework for the integration of data warehouse refresh process with the maintenance of materialized data mining views. Finally, we prove the feasibility of our approach by conducting several experiments on synthetic data sets.