Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems
ACM Transactions on Database Systems (TODS)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Extending the relational model to deal with fuzzy values
Fuzzy Sets and Systems
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fuzzy Association Rules: Semantic Issues and Quality Measures
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
A new method for computing fuzzy functional dependencies in relational database systems
Expert Systems with Applications: An International Journal
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This paper proposes fuzzy association rule which is a more generalized concept than boolean, quantitative, and interval association rules. Fuzzy association rule is a spectrum of definitions. Each particular fuzzy association rule can be defined by adding restrictions on the fuzziness depending on the needs of practical situations. The definition of fuzzy association rule also fills in the gap between fuzzy functional dependencies and clusters and results in a whole spectrum of concepts which is called data association spectrum. Such a unified view has practical implications. For example, various data mining problems can be converted to clustering problems and take advantage of the availability of a large number of good clustering algorithms.