Knowledge entropy in rough set theory

Author

Li, Ming ; Zhang, Xiao-Feng

Author_Institution

Inst. of Intelligent Inf. Process., Lanzhou Univ. of Technol., China

Volume

3

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

1408

Abstract

Rough set theory is an important tool to deal with imprecise, uncertainty and fuzzy information, and has gained great success in machine learning, data mining and intelligent data analysis. When it was proposed, many researchers worked on it in different views such as constructive methods, algebraic methods, formal concept analysis, and etc. We analyze it in view of entropy theory, and present a new concept - knowledge entropy. We discuss properties of it and apply it to discuss basic concepts in rough set theory. From the analysis, we construct a mapping between knowledge entropy theory and rough set theory, and we also hope to find effective algorithms by the application of it.

Keywords

data analysis; data mining; entropy; learning (artificial intelligence); rough set theory; algebraic methods; constructive methods; data mining; formal concept analysis; fuzzy information; intelligent data analysis; knowledge entropy theory; machine learning; rough set theory; Data mining; Educational institutions; Entropy; Fuzzy logic; Fuzzy set theory; Information processing; Learning systems; Machine learning; Set theory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1381994

Filename

1381994