Generating automatic fuzzy system from relational database system for estimating null values

There are many methods trying to do relational database estimations with a highly estimated accuracy rate by constructing a fuzzy learning algorithm automatically. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in a general fuzzy system. Thus, how to make the best compromise between the accuracy of the approximation and the degree of the interpretability is a significant study of the subject. In order to achieve the best compromise, this article attempts to propose a simple fuzzy learning algorithm to get a positive result in the relational database estimation on the real world database system, including partition determination, automatic membership function, and rule generation, and system approximation. © 2009 Taylor & Francis Group, LLC.