Membership Functions for a Fuzzy Relational Database: A Comparison of the Direct Rating and New Random Proportional Methods

Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a "Not" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2006

Author dc:creator

• Sanghi, Shweta

Contributors dc:contributor

• Dr. Lorraine M. Parker

• imprecise queries
• membership function
• fuzzy set theory
• relational model
• Data uncertainty
• Fuzzy Database
• Computer Sciences
• Physical Sciences and Mathematics

Statement dc:rights

• © The Author

Identifier

https://scholarscompass.vcu.edu/etd/1366

OAI identifier oai:identifier

oai:scholarscompass.vcu.edu:etd-2365