Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Fast discovery of association rules
Advances in knowledge discovery and data mining
On the Structure of Armstrong Relations for Functional Dependencies
Journal of the ACM (JACM)
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
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
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in 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
Towards mining frequent queries in star schemes
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Rewriting aggregate queries using functional dependencies
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
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The problem of mining all frequent queries on a relational table is a problem known to be intractable even for conjunctive queries. In this article, we restrict our attention to conjunctive projection-selection queries and we assume that the table to be mined satisfies a set of functional dependencies. Under these assumptions, we define and characterize two pre-orderings with respect to which the support measure is shown to be anti-monotonic. Each of these pre-orderings induces an equivalence relation for which all queries of the same equivalence class have the same support. The goal of this article is not to provide algorithms for the computation of frequent queries, but rather to provide basic properties of pre-orderings and their associated equivalence relations showing that functional dependencies can be used for an optimized computation of supports of conjunctive queries. In particular, we show that one of the two pre-orderings characterizes anti-monotonicity of the support, while the other one refines the former, but allows to characterize anti-monotonicity with respect to a given table, only. Basic computational implications of these properties are discussed in the article.