Application of a New Fast Algorithm for Getting Approximate Operators of Rough Set to Information System Classification

Author

Song, Ping ; Zhang, Xu

Author_Institution

Sch. of Inf. Eng., China Univ. of Geosci. (Beijing), Beijing, China

fYear

2010

fDate

17-19 Dec. 2010

Firstpage

673

Lastpage

676

Abstract

The information system classification is a crucial part of data mining, which aims to analysis the information system, extract important message from complex data, and forecast the future development trend of data. At present, there are many methods to classify the data, for example, Rough Set Theory, Decision Tree, Bayesian Network, Genetic Algorithm, etc. The method presented in this paper, based on ID3 Algorithm, associated with the combination of Rough Set Theory and Decision Tree Theory, uses the conditional attribute as the decision tree´s node to classify data in the information system. Moreover, a new fast algorithm for getting approximate operators is used in the information system classification to improve the efficiency.

Keywords

belief networks; data mining; decision trees; genetic algorithms; information systems; pattern classification; rough set theory; Bayesian network; ID3 Algorithm; data mining; decision tree theory; genetic algorithm; information system classification; rough set approximate operators; Algorithm design and analysis; Approximation algorithms; Approximation methods; Classification algorithms; Classification tree analysis; Information systems; approximate operators; decision tree; information system classification; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2010 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-8814-8

Electronic_ISBN

978-0-7695-4270-6

Type

conf

DOI

10.1109/ICCIS.2010.168

Filename

5709175