Association rules extraction based on support vector machines

被引：0

作者：

Ma, Chaoyang ^{[1
]}

Ren, Jia ^{[1
]}

Su, Hongye ^{[1
]}

Chu, Jian ^{[1
]}

机构：

[1] Zhejiang Univ, Natl Lab Ind Control Technol, Inst Adv Proc Control, Hangzhou 310027, Peoples R China

来源：

WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年

关键词：

association rule extraction; support vector clustering(SVC); support vector data description(SVDD);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：5928 / +

页数：2