Analysis of fuzzy class association rule mining based on Genetic Network Programming

Author

Chen, Ci ; Mabu, Shingo ; Yue, Chuan ; Shimada, Kaoru ; Hirasawa, Kotaro

Author_Institution

Grad. Sch. of Inf., Waseda Univ., Fukuoka, Japan

fYear

2009

fDate

18-21 Aug. 2009

Firstpage

3480

Lastpage

3484

Abstract

Classification rule mining is a practical data mining technique widely used in real world. In the previous work, we have put forward a fuzzy class association rule mining method based on genetic network programming and applied it to network intrusion detection system which proved its efficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs) with fixed fuzzy membership functions and with evolved fuzzy membership functions, but also between FCARMs with and without probability node transition are carried out. The aim of this paper is to provide experimental analysis on the characteristics of FCARMs with different implementations. Experimental results conducted on real world database, KDD99Cup and DAPRA98 database from MIT Lincoln Laboratory, are studied to verify the comparison.

Keywords

data mining; fuzzy set theory; genetic algorithms; probability; security of data; DAPRA98 database; FCARM; KDD99Cup database; MIT Lincoln Laboratory; data mining technique; fuzzy class association rule mining; fuzzy membership function; genetic network programming; network intrusion detection system; probability node transition; Association rules; Data mining; Databases; Economic indicators; Electronic mail; Fuzzy systems; Genetic mutations; Information analysis; Intrusion detection; Production systems; classification; fuzzy class association rule; fuzzy membership function; genetic network programming; network intrusion detection; probability transition;

fLanguage

English

Publisher

ieee

Conference_Titel

ICCAS-SICE, 2009

Conference_Location

Fukuoka

Print_ISBN

978-4-907764-34-0

Electronic_ISBN

978-4-907764-33-3

Type

conf

Filename

5335109