Automatic and incremental generation of membership functions

Authors:
Narjes Hachani;Imen Derbel;Habib Ounelli
Affiliations:
Faculty of Science of Tunis, Tunis, Tunisia;Faculty of Science of Tunis, Tunis, Tunisia;Faculty of Science of Tunis, Tunis, Tunisia
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

Citing 2
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Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fuzzy Databases: Modeling, Design, and Implementation

Fuzzy Databases: Modeling, Design, and Implementation

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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.