Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
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
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
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
A Thorough Experimental Study of Datasets for Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Effective elimination of redundant association rules
Data Mining and Knowledge Discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Prince: an algorithm for generating rule bases without closure computations
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
A novel approach for privacy mining of generic basic association rules
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Mining monolingual and bilingual corpora
Intelligent Data Analysis
Extracting compact and information lossless sets of fuzzy association rules
Fuzzy Sets and Systems
MAD-IDS: novel intrusion detection system using mobile agents and data mining approaches
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Towards a multiagent-based distributed intrusion detection system using data mining approaches
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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The extremely large number of association rules that can be drawn from - even reasonably sized datasets, bootstrapped the development of more acute techniques or methods to reduce the size of the reported rule sets. In this context, the battery of results provided by the Formal Concept Analysis (FCA) allowed one to define "irreducible" nuclei of association rule subset better known as generic bases. From such a condensed and reduced size set of association rules, it is possible to infer all association rules commonly via an adequate axiomatic system. In this paper, we introduce a novel informative generic basis of association rules, conveying two types of knowledge: "factual" and "implicative". We also present a valid and complete axiomatic system allowing one to infer the set of all association rules. Results of the experiments carried out on real-life datasets have shown important profits in terms of compactness of the introduced generic basis.