Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fuzzy logic in data modeling: semantics, constraints, and database design
Fuzzy logic in data modeling: semantics, constraints, and database design
Data mining: concepts and techniques
Data mining: concepts and techniques
Simple association rules (SAR) and the SAR-based rule discovery
Computers and Industrial Engineering
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
On the characterizations of fuzzy implications satisfying I(x,y)=I(x,I(x,y))
Information Sciences: an International Journal
On the first place antitonicity in QL-implications
Fuzzy Sets and Systems
The law of importation for discrete implications
Information Sciences: an International Journal
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This paper extends the work on discovering fuzzy association rules with degrees of support and implication (ARsi). The effort is twofold: one is to discover ARsi with hierarchy so as to express more semantics due to the fact that hierarchical relationships usually exist among fuzzy sets associated with the attribute concerned: the other is to generate a "core" set of rules, namely the rule cover set, that are of more interest in a sense that all other rules could be derived by the cover set. Corresponding algorithms for ARsi with hierarchy and the cover set are proposed along with pruning strategies incorporated to improve the computational efficiency. Some data experiments are conducted as well to show the effectiveness of the approach.