Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SQL-like language for database mining
ADBIS'97 Proceedings of the First East-European conference on Advances in Databases and Information systems
Incremental data mining using concurrent online refresh of materialized data mining views
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Data mining is an interactive and iterative process. Users issue series of similar queries until they receive satisfying results, yet currently available data mining systems do not support iterative processing of data mining queries and do not allow to re-use the results of previous queries. Consequently, mining algorithms suffer from long processing times, which are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are usually very similar. This observation leads to the idea of reusing materialized results of previous data mining queries. We present the notion of a materialized data mining view and we propose two novel algorithms which aim at efficient discovery of association rules in the presence of materialized results of previous data mining queries.