Data-driven fuzzy modeling has been used in a wide variety of applications. However, in fuzzy rule-based models acquired from numerical data, redundancy often exists in the form of redundant rules or similar fuzzy sets. This results in unnecessary structural complexity and decreases the interpretability of the system. In this paper, a rule-base self-extraction and simpliΓΏcation method is proposed to establish interpretable fuzzy models from numerical data. A fuzzy clustering technique associated with the proposed fuzzy partition validity index is used to extract the initial fuzzy rule-base and ΓΏnd out the optimal number of fuzzy rules. To reduce the complexity of fuzzy models while keeping good model accuracy, some approximate similarity measures are presented and a parameter ΓΏne-tuning mechanism is introduced to improve the accuracy of the simpliΓΏed model. Using the proposed similarity measures, the redundant fuzzy rules are removed and similar fuzzy sets are merged to create a common fuzzy set in the rule base. The simpliΓΏed rule base is computationally e cient and linguistically interpretable. The approach has been successfully applied to fuzzy models of non-linear function approximation, dynamical system modeling and mechanical property prediction for hot-rolled steels.