Fuzzy sets in approximate reasoning, part 2: logical approaches
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Weighted fuzzy production rules
Fuzzy Sets and Systems
Mining fuzzy association rules in databases
ACM SIGMOD Record
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering knowledge from fuzzy concept lattice
Data mining and computational intelligence
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
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
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
International Journal of Data Mining and Bioinformatics
A new generic basis of "factual" and "implicative" association rules
Intelligent Data Analysis
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
On the representation, measurement, and discovery of fuzzy associations
IEEE Transactions on Fuzzy Systems
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Applying classical association rule extraction framework on fuzzy datasets leads to an unmanageably highly sized association rule sets. Moreover, the discretization operation leads to information loss and constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such a drawback, this paper proposes the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. The presented approach relies on the extension, within the fuzzy context, of the notion of closure and Galois connection, that we introduce in this paper. In order to select without loss of information a generic subset of all fuzzy association rules, we define three fuzzy generic basis from which remaining (redundant) FARs are generated. This generic basis constitutes a compact nucleus of fuzzy association rules, from which it is possible to informatively derive all the remaining rules. In order to ensure a sound and complete derivation process, we introduce an axiomatic system allowing the complete derivation of all the redundant rules. The results obtained from experiments carried out on benchmark datasets are very encouraging. They highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.