CEC Faculty Articles

Title

Entropy Based Rule Derivation from Data with Uncertainty

Event Date/Location

Melbourne, Australia / 2001

Document Type

Article

Date

12-2001

Publication Title

Proceedings of the 10th IEEE International Conference on Fuzzy Systems

ISSN or ISBN

0-7803-7293-X

First Page

744

Last Page

748

Description

Due to its advantages, fuzzy data model has been widely used to model and represent data with uncertainty. More and more applications show the needs to explore the data with uncertainty and to perform tasks of knowledge discovery in fuzzy database. This paper presents an attribute-oriented and probabilistic entropy based approach to knowledge discovery from uncertain data. The probabilistic entropy with the weighted values of membership functions is used to measure the possibility from fuzzy data sets. Also, it is employed to derive the rules that characterize these data sets.

DOI

10.1109/FUZZ.2001.1009062

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