Abstract

Fuzzy logic has become a very popular method of reasoning a system with approximate input system instead of a precise one. When qualitative variables are used to determine the decisions then we have to create some specific functions where the membership values of the input can be any number between 0 to 1 instead of 1 or 0 which is used in binary logic. When number of input attribute increases it the combinatorial rules increases exponentially, and diminishes performance of the system. The problem is generally known as “combinatorial rule explosion”. The Information Technology Department of Minnesota State University, Mankato has been developing a system to analyze historical data and mining. The research paper presents a methodology to reduce the number of rules used in the application and creating a data prediction system using partial incomplete data set.

Advisor

Cyrus Azarbod

First Committee Member

Hamed Sallam

Second Committee Member

Mahbubur R. Syed

Date of Degree

2011

Language

english

Document Type

Thesis

Degree

Master of Science (MS)

Department

Computer Information Science

College

Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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