Learning fuzzy rules with their implication operators
Data & Knowledge Engineering
Fuzzy Sets and Systems
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
Refinement of temporal constraints in fuzzy associations
International Journal of Approximate Reasoning
Perception-based approach to time series data mining
Applied Soft Computing
Fuzzy Sets and Systems
Extracting compact and information lossless sets of fuzzy association rules
Fuzzy Sets and Systems
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Detection of fuzzy association rules by fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms (2012)
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
A fuzzy coherent rule mining algorithm
Applied Soft Computing
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The use of fuzzy sets to describe associations between data extends the types of relationships that may be represented, facilitates the interpretation of rules in linguistic terms, and avoids unnatural boundaries in the partitioning of the attribute domains. In addition, the partial membership values provide a method for incorporating the distribution of the data into the assessment of a rule. This paper investigates techniques to identify and evaluate associations in a relational database that are expressible by fuzzy if-then rules. Extensions of the classical confidence measure based on the α-cut decompositions of the fuzzy sets are proposed to incorporate the distribution of the data into the assessment of a relationship and identify robustness in an association. A rule learning strategy that discovers both the presence and the type of an association is presented.