A database perspective on knowledge discovery
Communications of the ACM
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Integration of Data Mining with Database Technology
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Inductive Logic Programming Query Language for Database Mining
AISC '98 Proceedings of the International Conference on Artificial Intelligence and Symbolic Computation
On Monotone Data Mining Languages
DBPL '01 Revised Papers from the 8th International Workshop on Database Programming Languages
Unifying Framework for Rule Semantics: Application to Gene Expression Data
Fundamenta Informaticae - Special issue ISMIS'05
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Splash: ad-hoc querying of data and statistical models
Proceedings of the 13th International Conference on Extending Database Technology
Towards an algebraic framework for querying inductive databases
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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We present a simple logical query language called R£ for expressing different kinds of rules and we study how this language behaves with respect to the well-known Armstrong's axioms. We point out some negative results, e.g. it is undecidable to know whether or not a query from this language is "Armstrong compliant". The main contribution of this paper is to exhibit a restricted form of R£-queries -- yet with a good expressive power -- for which Armstrong's axioms are sound. From this result, this sublanguage turns out to have structural and computational properties which have been shown to be very useful in data mining, databases and formal concept analysis.