Mining fuzzy association rules in databases
ACM SIGMOD Record
A fuzzy approach for mining quantitative association rules
Acta Cybernetica
A fuzzy expert system design for diagnosis of prostate cancer
CompSysTech '03 Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Design of a fuzzy expert system for determination of coronary heart disease risk
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
A hybrid fuzzy-neural expert system for diagnosis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Fuzzy-evolutionary synergism in an intelligent medical diagnosis system
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Fuzzy versus quantitative association rules: a fair data-driven comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
On Sharp Boundary Problem in Rule Based Expert Systems in the Medical Domain
International Journal of Healthcare Information Systems and Informatics
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In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acquire a knowledge-base, which corresponds more intuitively to human perception with a high comprehensibility. This approach reduces the number of rules in the knowledge-base when compared with the Standard Rule-base Formulation (SRF) and makes possible the rating of the rules according to their relevance. The rule relevance is determined by the measures of significance and certainty factors. The approach is validated using a medical database and the result shows that this approach ultimately reduces the number of rules and enhances the comprehensibility of the expert system.