Association Rules Extraction Based on Support Vector Machines

Author

Ma, Chaoyang ; Ren, Jia ; Su, Hongye ; Chu, Jian

Author_Institution

Inst. of Adv. Process Control, Zhejiang Univ., Hangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5928

Lastpage

5932

Abstract

A new method of association rules extraction based on SVM is proposed in this paper. The SVC and data description is used to analyze the sample data, and the obtained support vectors are used to get the association rules in this method. It takes advantage of the abilities of SVM sufficiently which can deal with limited samples, nonlinear data and have a good generalization performance. At the same time it overcomes the unintelligible problem of SVM´s classifiable function. And the program efficiency is improved by introducing the classic SMO algorithm. Simulations based on industrial data have been done and the results show great effectiveness of this proposed modeling approach which provides a novel thought to get the association rules

Keywords

data description; data mining; pattern clustering; support vector machines; SMO algorithm; association rules extraction; nonlinear data; program efficiency; support vector clustering; support vector data description; support vector machines; Association rules; Chaos; Data analysis; Data mining; Industrial control; Laboratories; Process control; Static VAr compensators; Support vector machine classification; Support vector machines; Association rule extraction; support vector clustering(SVC); support vector data description(SVDD);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714216

Filename

1714216