Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
A database perspective on knowledge discovery
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Composition of Mining Contexts for Efficient Extraction of Association Rules
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Materialized Data Mining Views
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A model for managing collections of patterns
Proceedings of the 2007 ACM symposium on Applied computing
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Mining frequent queries often requires the repeated execution of some extraction algorithm for different values of the support, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for iterative mining, whereby mining results already obtained are re-used to accelerate subsequent steps in the mining process. In this paper, we present an approach for the iterative mining of frequent queries. Our approach is based on the notion of mining context, where a mining context is a set of queries over the same schema. We define operations on mining contexts, based on the standard relational algebra, and we also introduce new operators, one of which for computing frequent queries. We first study the properties of the operators, then we consider particular mining contexts using biases for which frequent queries can be computed using any level-wise algorithm. Iterative mining is obtained by combining these particular contexts using our set of operations. We have implemented our approach and conducted experiments that show its efficiency in mining frequent queries.