Compensatory Fuzzy Logic (CFL) is a logical system, which enables an optimal way for the modeling of knowledge. Its axiomatic character enables the work of natural language translation of logic, so it is used in knowledge discovery and decision-making. In this work we propose a general and flexible approach for knowledge discovery which allows obtaining different knowledge structure using a metaheuristic approach. The proposed method was tested by experimental analysis from a data set, using a tool developed in visual Prolog. The experimental results show some advantages of the proposed approach for representing patterns and trends from data.

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