The design of relational databases
The design of relational databases
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Computational problems related to the design of normal form relational schemas
ACM Transactions on Database Systems (TODS)
Minimum Covers in Relational Database Model
Journal of the ACM (JACM)
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discrete Applied Mathematics - Special issue: The 1998 conference on ordinal and symbolic data analysis (OSDA '98)
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
On Armstrong-compliant logical query languages
Proceedings of the 4th International Workshop on Logic in Databases
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The notion of rules is very popular and appears in different flavors, for example as association rules in data mining or as functional dependencies in databases. Their syntax is the same but their semantics widely differs. In the context of gene expression data mining, we introduce three typical examples of rule semantics and for each one, we point out that Armstrong's axioms are sound and complete. In this setting, we propose a unifying framework in which any "well-formed" semantics for rules may be integrated. We do not focus on the underlying data mining problems posed by the discovery of rules, rather we prefer to discuss the expressiveness of our contribution in a particular application domain: the understanding of gene regulatory networks from gene expression data. The key idea is that biologists have the opportunity to choose - among some predefined semantics - or to define the meaning of their rules which best fits into their requirements. Our proposition has been implemented and integrated into an existing open-source system named MeV of the TIGR environment devoted to microarray data interpretation. An application has been performed on expression profiles of a sub-sample of genes from breast cancer tumors.