Data Reduction using Similarity Class and Enhanced Tolerance Relation for Complete and Incomplete Information Systems

Author(s):  
Mustafa Mat Deris ◽  
Mariam A. Hamid ◽  
Noraini Ibrahim ◽  
Riswan Efendi ◽  
Iwan Tri Riyadi Yanto
2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Do Van Nguyen ◽  
Koichi Yamada ◽  
Muneyuki Unehara

This paper discusses and proposes a rough set model for an incomplete information system, which defines an extended tolerance relation using frequency of attribute values in such a system. It first discusses some rough set extensions in incomplete information systems. Next, “probability of matching” is defined from data in information systems and then measures the degree of tolerance. Consequently, a rough set model is developed using a tolerance relation defined with a threshold. The paper discusses the mathematical properties of the newly developed rough set model and also introduces a method to derive reducts and the core.


Author(s):  
JIYE LIANG ◽  
ZONGBEN XU

Rough set theory is emerging as a powerful tool for reasoning about data, knowledge reduction is one of the important topics in the research on rough set theory. It has been proven that finding the minimal reduct of an information system is a NP-hard problem, so is finding the minimal reduct of an incomplete information system. Main reason of causing NP-hard is combination problem of attributes. In this paper, knowledge reduction is defined from the view of information, a heuristic algorithm based on rough entropy for knowledge reduction is proposed in incomplete information systems, the time complexity of this algorithm is O(|A|2|U|). An illustrative example is provided that shows the application potential of the algorithm.


Sign in / Sign up

Export Citation Format

Share Document