NDLI: Association Rules Extraction Based on Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Chaoyang Ma Jia Ren Hongye Su Jian Chu
Copyright Year	2006
Description	Author affiliation: Inst. of Adv. Process Control, Zhejiang Univ., Hangzhou (Chaoyang Ma; Jia Ren; Hongye Su; Jian Chu)
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
Starting Page	5928
Ending Page	5932
File Size	265080
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Support vector machines Data mining Chaos Laboratories Industrial control Process control Static VAr compensators Data analysis Support vector machine classification support vector data description(SVDD) Association rule extraction support vector clustering(SVC)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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