Distribution Reduction in Inconsistent Interval Ordered Information Systems Based on Dominance Relations

Author

Hong Wang ; Ming-gang Du

Author_Institution

Coll. of Math. & Comput. Sci., Shanxi Normal Univ., Linfen, China

fYear

2013

fDate

14-15 Dec. 2013

Firstpage

363

Lastpage

367

Abstract

Rough sets serves as a tool for data analysis and knowledge discovery from data databases. Attribute reduction is a basic issue in knowledge representation and data mining. This paper deals with distribution reduction in an inconsistent interval ordered information systems. The distribution reduction and maximum distribution reduction are proposed in inconsistent interval ordered information systems. Moreover, properties and relationship between them are discussed. Furthermore, judgement theorem and discernibility matrix are obtained, from which approaches to distribution reductions can be provided in inconsistent interval ordered information systems.

Keywords

data mining; knowledge representation; matrix algebra; rough set theory; attribute reduction; data analysis; data databases; data mining; discernibility matrix; dominance relations; inconsistent interval ordered information system; judgement theorem; knowledge discovery; knowledge representation; maximum distribution reduction; rough sets; Approximation methods; Data mining; Educational institutions; Gold; Information systems; Rough sets; Distribution reduction; Interval information systems; Maximum distribution reduction; Rough sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2013 9th International Conference on

Conference_Location

Leshan

Print_ISBN

978-1-4799-2548-3

Type

conf

DOI

10.1109/CIS.2013.83

Filename

6746419