Characterizations of attributes in generalized approximation representation spaces

Authors:
Guo-Fang Qiu;Wen-Xiu Zhang;Wei-Zhi Wu
Affiliations:
The College of Management, Zhejiang University, Hangzhou, Zhejiang, P.R.China;Faculty of Science, Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China;Information College, Zhejiang Ocean University, Zhoushan, Zhejiang, P.R.China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

Citing 6
Cited 2

Variable precision rough set model

Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Data Analysis and Mining in Ordered Information Tables

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Approximate Reducts and Association Rules - Correspondence and Complexity Results

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Approaches to knowledge reduction based on variable precision rough set model

Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Knowledge reduction in random information systems via Dempster-Shafer theory of evidence

Information Sciences: an International Journal

Decision-theoretic rough set models

RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A general definition of an attribute reduct

RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss characterizations of three important types of attribute sets in generalized approximation representation spaces, in which binary relations on the universe are reflexive. Many information tables, such as consistent or inconsistent decision tables, variable precision rough set models, consistent decision tables with ordered valued domains and with continuous valued domains, and decision tables with fuzzy decisions, can be unified to generalized approximation representation spaces. A general approach to knowledge reduction based on rough set theory is proposed.