Publication Date
2006
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
Rough Set theory is a mathematical theory for classification based on structural analysis of relational data. It can be used to find the minimal reduct. Minimal reduct is the minimal knowledge representation for the relational data. The theory has been successfully applied to various domains in data mining. However, a major limitation in Rough Set theory is that finding the minimal reduct is an NP-hard problem. C4.5 is a very popular decision tree-learning algorithm. It is very efficient at generating a decision tree. This project uses the decision tree generated by C4.5 to find the optimal reduct for a relational table. This method does not guarantee finding a minimal reduct, but test results show that the optimal reduct generated by this approach is equivalent or very close to the minimal reduct.
Recommended Citation
Li, Xin, "Finding Optimal Reduct for Rough Sets by Using a Decision Tree Learning Algorithm" (2006). Master's Projects. 125.
DOI: https://doi.org/10.31979/etd.nuhk-2kpp
https://scholarworks.sjsu.edu/etd_projects/125
