REVIEW ARTICLE
Tendency Mining in Dynamic Association Rules Based on SVM Classifier
Zhonglin Zhang*, Zongcheng Liu, Chongyu Qiao
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 303
Last Page: 307
Publisher Id: TOMEJ-8-303
DOI: 10.2174/1874155X01408010303
Article History:
Received Date: 25/07/2014Revision Received Date: 04/08/2014
Acceptance Date: 04/08/2014
Electronic publication date: 16/9/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the obtained CARs taking advantage when dealing with high complex data. It uses a method based on judging rules’ weight to construct the model. At last, the method is compared with the traditional methods with respect to the mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results, it is proved that the method has lower complexity and higher classification accuracy.
