What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Implication-Based Fuzzy Association Rules
PKDD '01 Proceedings of the 5th European Conference 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
Applied Intelligence
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Fuzzy Sets and Systems
Mining pure linguistic associations from numerical data
International Journal of Approximate Reasoning
Linguistic data mining with fuzzy FP-trees
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
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 is a contribution to theoretical background of data mining, more precisely to fuzzy association analysis. We consider three the most commonly used confirmation measures and we study relations among found and known associations given by them. Good understanding of such relationships is necessary for creating more efficient algorithms or for subsequent work with found associations as well as for cooperation with the consumer of the data mining process. Even if our motivation to this work arose from mining of linguistic associations, found properties that coincide with semantics of mined associations are valid in general. Additionally, some examples showing how to use obtained properties are also contained in this paper.