Fast discovery of association rules
Advances in knowledge discovery and data mining
An Extension to SQL for Mining Association Rules
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
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Relational Association Rules: Getting WARMeR
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining for Tree-Query Associations in a Graph
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining all frequent projection-selection queries from a relational table
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining frequent conjunctive queries in star schemas
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
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We present an algorithm for mining frequent queries in arbitrary relational databases, over which functional dependencies are assumed. Building upon previous results, we restrict to the simple, but appealing subclass of simple conjunctive queries. The proposed algorithm makes use of the functional dependencies of the database to optimise the generation of queries and prune redundant queries. Furthermore, our algorithm is capable of detecting previously unknown functional dependencies that hold on the database relations as well as on joins of relations. These detected dependencies are subsequently used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL, and provide several promising experimental results.