Abstract
The fuzzy set theory is well known as the most adequate framework to model and manage vague terms. In real-world fuzzy data-base applications, such terms allow to express flexible queries. Moreover, due to the frequent updates of databases, it is highly desirable to perform these updates incrementally. In this paper, we propose an automatic and incremental approach for the generation of membership functions describing vague terms. This approach uses a clustering algorithm to identify automatically the number of these membership functions. Also, it is based on a density function in order to derive the cores of fuzzy sets.
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Hachani, N., Derbel, I., Ounelli, H. (2010). Automatic and Incremental Generation of Membership Functions. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_13
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DOI: https://doi.org/10.1007/978-3-642-13208-7_13
Publisher Name: Springer, Berlin, Heidelberg
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