The Method for Data Reduction Based on Evaluation of Attribute Significance

Author

He, Chaobo ; Chen, Qimai

Author_Institution

Dept. of Comput. Sci. & Eng., Zhongkai Univ. of Agric. & Eng., Guangzhou, China

fYear

2010

fDate

22-23 May 2010

Firstpage

1

Lastpage

4

Abstract

According to the problem of attribute subset selection, the paper put forward a method based on evaluation of attribute significance. Based on the rough set theories the method first defined the calculation formula of attribute significance and designed the corresponding solution algorithm, whose running time complexity decreased about |U|² orders of magnitude comparing with the similar algorithm on the same test dataset with |U| records. The result of application example shows that this method can reserve the condition attributes, which are important for decision attributes, and also can perform the data reduction operation effectively.

Keywords

data mining; data reduction; rough set theory; attribute significance; attribute subset selection; data reduction; rough set theory; running time complexity; Agricultural engineering; Agriculture; Application software; Chaos; Computer science; Data engineering; Data mining; Design engineering; Helium; Set theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5872-1

Electronic_ISBN

978-1-4244-5874-5

Type

conf

DOI

10.1109/IWISA.2010.5473715

Filename

5473715