Fuzzy Concepts in Expert Systems
Computer
Fuzzy logic and knowledge representation using linguistic modifiers
Fuzzy logic for the management of uncertainty
Measures of entropy and fuzziness related to aggregation operators
Information Sciences—Intelligent Systems: An International Journal
A fuzzy expert system approach for real-time monitoring of endemic diseases
Information Sciences—Applications: An International Journal
Data mining and knowledge discovery in databases
Communications of the ACM
Rectangular decomposition heuristics for documentary databases
Information Sciences: an International Journal
An Introduction to Fuzzy Logic Applications in Intelligent Systems
An Introduction to Fuzzy Logic Applications in Intelligent Systems
Comparative evaluation of the discovered knowledge
IEA/AIE'1997 Proceedings of the 10th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
A multi-viewpoint system to support abductive reasoning
Information Sciences: an International Journal
An improved rectangular decomposition algorithm for imprecise and uncertain knowledge discovery
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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Discovering knowledge from databases in order to classify new patterns is an interesting field for machine learning methods. Particularly, rule induction approaches constitute prominent machine learning methods that lead to avoid the disadvantages of the decision tree. The fuzzy incremental production rule (FIPR) based system is a rule induction system that generates imprecise and uncertain IF-THEN rules from data records. It allows the incremental maintenance of the knowledge base with a minimal overhead. The precision analysis with real world data sets, and the complexity analysis are used to compare this system with existing ones and to prove the usefulness of fuzzy knowledge representation.