Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
ACM Computing Surveys (CSUR)
On condensed representations of constrained frequent patterns
Knowledge and Information Systems
Data Mining and Knowledge Discovery
Succinct system of minimal generators: a thorough study, limitations and new definitions
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
SARM — succinct association rule mining: an approach to enhance association mining
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining succinct systems of minimal generators of formal concepts
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
About the lossless reduction of the minimal generator family of a context
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Succinct system of minimal generators: a thorough study, limitations and new definitions
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
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In knowledge mining, current trend is witnessing the emergence of a growing number of works towards defining "concise and lossless" representations. One main motivation behind is: tagging a unified framework for drastically reducing large sized sets of association rules. In this context, generic bases of association rules - whose backbone is the conjunction of the concepts of minimal generator (MG) and closed itemset (CI) - constituted so far irreducible compact nuclei of association rules. However, the inherent absence of a unique MG associated to a given CI offers an "ideal" gap towards a tougher redundancy removal even from generic bases of association rules. In this paper, we adopt the succinct system of minimal generators (SSMG), as newly redefined in [1], to be an exact representation of the MG set. Then, we incorporate the SSMG into the framework of generic bases to only maintain the succinct generic association rules. After that, we give a thorough formal study of the related inference mechanisms allowing to derive all redundant association rules starting from succinct ones. Finally, an experimental study shows that our approach makes it possible to eliminate without information loss an important number of redundant generic association rules and thus, to only present succinct and informative ones to users.