An analysis of first-order logics of probability
Artificial Intelligence
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Mining fuzzy association rules in databases
ACM SIGMOD Record
An effective algorithm for mining interesting quantitative association rules
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning Logical Definitions from Relations
Machine Learning
Fuzzy Association Rules: Semantic Issues and Quality Measures
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Learning first order fuzzy logic rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
On the representation, measurement, and discovery of fuzzy associations
IEEE Transactions on Fuzzy Systems
Data & Knowledge Engineering
A phenotypic genetic algorithm for inductive logic programming
Expert Systems with Applications: An International Journal
Systemic approach to fuzzy logic formalization for approximate reasoning
Information Sciences: an International Journal
Importance weighting and andness control in De Morgan dual power means and OWA operators
Fuzzy Sets and Systems
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
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Fuzzy predicates have been incorporated into machine learning and data mining to extend the types of data relationships that can be represented, to facilitate the interpretation of rules in linguistic terms, and to avoid unnatural boundaries in partitioning attribute domains. The confidence of an association is classically measured by the co-occurrence of attributes in tuples in the database. The semantics of fuzzy rules, however, is not co-occurrence but rather graduality or certainty and is determined by the implication operator that defines the rule. In this paper we present a learning algorithm, based on inductive logic programming, that simultaneously learns the semantics and evaluates the validity of fuzzy rules. The learning algorithm selects the implication that maximizes rule confidence while trying to be as informative as possible. The use of inductive logic programming increases the expressive power of fuzzy rules while maintaining their linguistic interpretability.