Fundamentals of database systems (2nd ed.)
Fundamentals of database systems (2nd ed.)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh 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
Incremental Refinement of Mining Queries
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
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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Many frequent itemset discovery algorithms have been proposed in the area of data mining research. The algorithms exhibit significant computational complexity, resulting in long processing times. Their performance is also dependent on source data characteristics. We argue that users should not be responsible for choosing the most efficient algorithm to solve a particular data mining problem. Instead, a data mining query optimizer should follow the costbased optimization rules to select the appropriate method to solve the user's problem. The optimizer should consider alternative data mining algorithms as well as alternative data access paths. In this paper, we use the concept of materialized views to describe possible data access paths for frequent itemset discovery.