Finding fuzzy classification rules using data mining techniques
Pattern Recognition Letters
A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
Journal of the American Society for Information Science and Technology
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
Fuzzy Optimization and Decision Making
Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining
Information Sciences—Informatics and Computer Science: An International Journal
Design and application of hybrid intelligent systems
Genetic fuzzy discretization with adaptive intervals for classification problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Finding useful fuzzy concepts for pattern classification using genetic algorithm
Information Sciences: an International Journal
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Feature Selection in Genetic Fuzzy Discretization for the Pattern Classification Problems
IEICE - Transactions on Information and Systems
Computers and Industrial Engineering
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A single-objective genetic-fuzzy approach for multi-objective fuzzy problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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When we generate association rules, continuous attributes have to be discretized into intervals while our knowledge representation is not always based on such discretiztion.Forexample, we usually use some linguistic terms (e.g., young, middle age, and old) for dividing our ages into somefuzzy categories.In this paper, we describe the extraction of linguistic association rules and examine the performanceof extracted rules.First we modify the definitions of the two basic measures (i.e., confidence and support) ofassociation rules for extracting linguistic association rules. The main difference between standard and linguistics association rules is the discretiztion of continuous attributes. We divide the domain interval of each attribute into some Fuzzy discretiztion with standard on-fuzzy discretiztion Through computer simulations on a pattern classificationproblem with many continuous attributes.The classification performance of extracted rules on unseen test patterns is examined under various conditions.Simulation results show that linguistic association rules with rule weights have highgeneralization ability even when the domain of each continuous attribute is homogeneously partitioned.