Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
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
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
A Practical Approach to Microarray Data Analysis
A Practical Approach to Microarray Data Analysis
Frequent pattern discovery without binarization: mining attribute profiles
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery 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 (or multivalued) dependencies in databases. Their syntax is the same but their semantics widely differs. In this article, we focus on semantics for which 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 emphasize the expressiveness of our contribution in a particular domain of application: 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.