Measurement of membership functions and their acquisition
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Fuzzy logic for the management of uncertainty
Knowledge discovery in databases: an overview
AI Magazine
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The intensity of implication, a measurement learning machine
IEA/AIE '95 Proceedings of the 8th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Implication-Based Fuzzy Association Rules
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Interestingness Measures for Fuzzy Association Rules
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
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Considering a fuzzy knowledge discovery system we have realized we describe here the main features of such systems. First, we consider possible methods to define fuzzy partitions on numerical attributes in order to replace continuous or symbolic attributes by fuzzy ones. We explain then how to generalize statistical indexes to evaluate fuzzy rules, detailing a special index, the intensity of implication and its generalization to fuzzy rules. We describe then one algorithm use to extract fuzzy rules. Since many fuzzy operators are available, we propose a method to choose one fuzzy conjunction, one fuzzy implication and one fuzzy aggregation, and we explain how this choice may be validated by comparing the results of the Generalized Modus Ponens applied on the premises of the examples to the effective conclusions in the database. To reduce the important number of fuzzy rules extracted, we consider also some methods to aggregate fuzzy rules, showing that usage of classical reduction schemes requires specific choices of fuzzy operators.